This document discusses student retention in Massive Open Online Courses (MOOCs). It begins by providing background on the rapid growth of MOOCs and their extremely high dropout rates. The document then outlines a study that uses text mining, opinion mining, and machine learning techniques to build explanatory and predictive models of the factors that influence student retention in MOOCs. These factors include characteristics of students, courses, platforms, and universities. The study aims to identify the most important determinants of retention and provide insights to improve MOOC and online education design.
The paper examines student engagement in blended learning environments in higher business education in Australia. It presents findings from a student survey on engagement in both face-to-face and online components. The survey focused on student engagement from an online perspective based on six key dimensions, and findings are presented across various themes related to student perceptions. The paper provides valuable insights for blended learning design and practice.
This document summarizes a research study that examined how participation in an open educational resources (OER) training network for teachers impacted pedagogy. Key findings included:
1) The OER network facilitated knowledge sharing between teachers as they collaborated, discussed challenges and ideas, and shared lessons and resources.
2) Participation in the network reduced isolation and supported multidisciplinary work between teachers.
3) Access to OER provided teachers with adaptable lessons and best practices that they could incorporate into their own teaching.
"Innovating an integrated approach to collaborative eLearning practices in hi...eraser Juan José Calderón
"Innovating an integrated approach to collaborative eLearning practices in higher education: the case study of a corporate communication e-platform" de Cindy Sing Bik Ngai , Wing Man Lee, Patrick Pak Kei Ng and Doreen Dongying Wu Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hong Kong, People’s Republic of China aparecido en el STUDIES IN HIGHER EDUCATION https://doi.org/10.1080/03075079.2018.1482266
This study examines the impact of factors like class attendance, online course supplements from Khan Academy, and homework on student performance in quantitative business courses. The researchers collected data from university records on student attendance, homework scores, Khan Academy assignments completed, exam scores, and time taken to complete exams for courses in business statistics and operations management. They used two estimation methods - ordinary least squares regression and fixed effects regression - to analyze the data. The fixed effects method controls for unobserved differences between students. The results from fixed effects regression suggest that (1) class attendance positively impacts exam scores, (2) completing more Khan Academy assignments increases exam completion time but does not significantly impact scores, (3) spending more time on homework increases exam completion time
In Search of Quality: Using Quality Matters to Analyze the Quality of Massive...Patrick Lowenthal
The concept of the massive, open, online course (MOOC) is not new, but high-profile initiatives have moved them into the forefront of higher education news over the past few years. Members of institutions of higher education have mixed feelings about MOOCs, ranging from those who want to offer college credit for the successful completion of MOOCs to those who fear MOOCs are the end of the university as we know it. We set forth to investigate the quality of MOOCs by using the Quality Matters quality control framework. In this paper, we present the results of our inquiry, with a specific focus on the implications the results have on day-today practice of designing online courses.
De los santos, stephanie bain the lecture method is d e-a-d focus v10 n1 2016William Kritsonis
Published by NATIONAL FORUM JOURNALS - A group of national refereed, peer-reviewed, scholarly, academic periodicals. William Allan Kritsonis, PhD, Editor-in-Chief, NFJ (Since 1982)
The paper examines student engagement in blended learning environments in higher business education in Australia. It presents findings from a student survey on engagement in both face-to-face and online components. The survey focused on student engagement from an online perspective based on six key dimensions, and findings are presented across various themes related to student perceptions. The paper provides valuable insights for blended learning design and practice.
This document summarizes a research study that examined how participation in an open educational resources (OER) training network for teachers impacted pedagogy. Key findings included:
1) The OER network facilitated knowledge sharing between teachers as they collaborated, discussed challenges and ideas, and shared lessons and resources.
2) Participation in the network reduced isolation and supported multidisciplinary work between teachers.
3) Access to OER provided teachers with adaptable lessons and best practices that they could incorporate into their own teaching.
"Innovating an integrated approach to collaborative eLearning practices in hi...eraser Juan José Calderón
"Innovating an integrated approach to collaborative eLearning practices in higher education: the case study of a corporate communication e-platform" de Cindy Sing Bik Ngai , Wing Man Lee, Patrick Pak Kei Ng and Doreen Dongying Wu Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hong Kong, People’s Republic of China aparecido en el STUDIES IN HIGHER EDUCATION https://doi.org/10.1080/03075079.2018.1482266
This study examines the impact of factors like class attendance, online course supplements from Khan Academy, and homework on student performance in quantitative business courses. The researchers collected data from university records on student attendance, homework scores, Khan Academy assignments completed, exam scores, and time taken to complete exams for courses in business statistics and operations management. They used two estimation methods - ordinary least squares regression and fixed effects regression - to analyze the data. The fixed effects method controls for unobserved differences between students. The results from fixed effects regression suggest that (1) class attendance positively impacts exam scores, (2) completing more Khan Academy assignments increases exam completion time but does not significantly impact scores, (3) spending more time on homework increases exam completion time
In Search of Quality: Using Quality Matters to Analyze the Quality of Massive...Patrick Lowenthal
The concept of the massive, open, online course (MOOC) is not new, but high-profile initiatives have moved them into the forefront of higher education news over the past few years. Members of institutions of higher education have mixed feelings about MOOCs, ranging from those who want to offer college credit for the successful completion of MOOCs to those who fear MOOCs are the end of the university as we know it. We set forth to investigate the quality of MOOCs by using the Quality Matters quality control framework. In this paper, we present the results of our inquiry, with a specific focus on the implications the results have on day-today practice of designing online courses.
De los santos, stephanie bain the lecture method is d e-a-d focus v10 n1 2016William Kritsonis
Published by NATIONAL FORUM JOURNALS - A group of national refereed, peer-reviewed, scholarly, academic periodicals. William Allan Kritsonis, PhD, Editor-in-Chief, NFJ (Since 1982)
Gamification Strategies in a Hybrid Exemplary College CourseSzymon Machajewski
Using technology in teaching and learning finds a wide adoption in recent years. 63.3% of chief academic leaders surveyed by the Babson Survey Research Group confirm that online education is critical to their long-term strategy. Modern engagement pedagogies, such as digital gamification, hold a promise of shaping student experience. While course builders and instructors investigate new technologies and teaching methods questions arise about the instructional quality of academic courses with online content or with gamification elements. In addition, students are not the digital natives many hoped them to be. 83% of millennials report sleeping with their smartphones, but 58% have poor skills in solving problems with technology. This paper reports on a gamefully designed course, delivered in a hybrid modality, which was selected through a peer review process as an exemplary course in consideration of instructional design. The course was evaluated according to the Blackboard Exemplary Course Program rubric. Gamification was introduced in three phases: player onboarding phase, player scaffolding phase, and player endgame. Various technologies involved in the course included: MyGame gamification mobile app, Blackboard Learn, Cengage Skills Assessment Manager, Kahoot, Amazon Alexa, Google Traveler, Twitter, and others. The course focused on gamification according to the short and long game theory to engage students during lectures (short game) and throughout the semester (long game).
Faculty attitudes towards integrating technology and innovationIJITE
Technological innovation is an important aspect of teaching and learning in the 21st century. This article
examines faculty attitudes toward technology use in the classroom at one regional public university in the
United States. Building on a faculty-led initiative to develop a Community of Practice for improving
education, this study used a mixed-method approach of a faculty-developed, electronic survey to assess this
topic. Findings from 72 faculty members revealed an overall positive stance toward technology in the
classroom and the average faculty member utilized about six technology tools in their courses. The
opportunities, barriers and future uses for technologies in the higher education classroom emerged from
the open-ended questions on the survey. One finding of particular concern is that faculty are fearful that
technology causes a loss of the humanistic perspective in education. The university is redesigning ten of its
most popular courses to increase flexibility, accessibility and student success.
Ziyanak, sebahattin the effectiveness of survey instruments nfaerj v29 n3 2016William Kritsonis
This article examines how sociological imagination of the individuals living in southeastern Turkey is constructed through Movie, The Bliss. Traditional and modern forms of life are symbolically constructed in this movie. The framework of “honor killing,” “masculinity in southeastern Turkey," “cultural deficiency,” and “othering” will be analyzed to explicate how stereotypical southeastern characters are reproduced. Content analysis technique is applied to interpret apparent and latent contents, contexts, aspects and so forth. Developed categories are revisited through Ibn Khaldun's Typology, cultural deficiency theory, Tonnies’ theory, Durkheim’s view on society, and Goffman’s framing process.
William Allan Kritsonis, PhD - Editor-in-Chief, NATIONAL FORUM JOURNALS (Established 1982)
This document provides an overview of Russell Francis's doctoral thesis investigating the implications of media change for learning. The thesis explored how learners are appropriating new media to support learning through two design experiments and an ethnographic study of advanced learners. The ethnographic study identified six genres of practice used by learners and attempted to develop conceptual tools to understand new media literacies. The thesis argues that media convergence has decentralized formal education and learners now need new media literacies to design personal learning environments leveraging online resources.
Administrative Support of Faculty Preparation and Interactivity in Online Tea...William Kritsonis
Administrative Support of Faculty Preparation and Interactivity in Online Teaching: Factors in Student Success by Dr. Jon E. Travis and Grace Rutherford - Published in NATIONAL FORUM JOURNALS - www.nationalforum.com - Dr. William Allan Kritsonis, Editor-in-Chief
This document summarizes a study on how first year students at Amsterdam University of Applied Sciences use Facebook. Previous research identified four major Facebook activities: information sharing, sharing for educational purposes, social purposes, and leisure. The study surveyed 618 first year students about their Facebook activities and background characteristics. The survey results were analyzed using statistical software to determine if certain Facebook activities correlated with student characteristics like gender, age, or socioeconomic status. Understanding how students use Facebook could provide insights into its relationship with student success in higher education.
Dr. Diane Hamilton explores the relationship between media choices and teach experience in online courses. This presentation won best award at the CSI conference.
Education 4.0 and Computer Science: A European perspectiveBart Rienties
This systematic literature review aimed at identifying the pedagogical approaches, aligned with Education 4.0, used to support teaching computer science courses with undergraduate and graduate students in Europe. A three-step coding process was conducted to identify and analyse 20 papers. Quantitative and qualitative analysis of the selected papers revealed a three-cluster solution with common characteristics that could be used to describe those pedagogical approaches. The review also showed that the term Education 4.0 is still relatively new and has not been conceptualised in terms of computer science courses, although the characteristics of Education 4.0 are visible throughout the pedagogical approaches.
Bart Rienties, Rebecca Ferguson, Christothea Herodotou, Francisco Iniesto, Julia Sargent, Igor Balaban, Henry Muccini, Sirje Virkus
This document discusses a study that examined faculty involvement in blended instruction and their attitudes towards it at universities. The study surveyed coordinators and faculty at extensive doctoral research universities. It found that the most common blended method was supplementary online materials for face-to-face classes. Faculty were actively involved in designing online content and had positive attitudes towards blended instruction. Universities provided support like online help desks but faced challenges from high faculty workloads and lack of motivation to adopt blended models.
University students were surveyed about their use of digital technologies for self-regulated learning. The results showed that:
1) Students frequently use the internet, communication tools, and repositories to search for information and exchange social support, but tend not to use technologies to regulate their own learning process.
2) The most common self-regulated learning strategies used with technology were related to social support, such as sharing materials and commenting on information.
3) Two distinct groups of students were identified that differed in their use of self-regulated learning strategies with technology, with some groups making more use of these strategies than others.
Cognitive Communication 2.0 in the Classroom - Resonance of an Experience in ...Cornélia Castro
This document summarizes a study that evaluated the integration of Twitter into PowerPoint presentations in university classrooms. The study aimed to assess how this approach could transform one-way communication between the teacher and many students (cognitive communication 1.0) into more interactive communication between all participants (cognitive communication 2.0). A questionnaire was administered to 122 university students after they experienced a PowerPoint presentation integrated with Twitter in their classroom. The questionnaire assessed the approach across several dimensions related to pedagogy, technology, learning, classroom interactions, and behaviors. Preliminary results found that students recognized the pedagogical and technological benefits of the approach and perceived positive effects on the quality of their learning and interactions in the classroom.
Oumh1103 pdf factor influencing adult learners' decicion to drop out or per...Mohamad Kelana Mat
1) The study examines factors that influence adult learners' decisions to drop out of or persist in online courses, comparing 147 learners who dropped out versus those who completed courses.
2) It finds that dropouts and completers differ significantly in their perceptions of family and organizational support as well as their satisfaction and perception of course relevance.
3) A theoretical framework including individual characteristics, family/organizational support, satisfaction, and relevance can predict whether learners will drop out or persist, with organizational support and relevance being particularly predictive. The results suggest lower dropout rates may result from enhancing course relevance and support for learners.
A cross-national study of teacher’s perceptions of online -2016.pdfJackeline Ferreira
This study examines perceptions of success factors in online learning from instructors' perspectives across four universities in different countries. It uses a model that considers input factors related to students, process factors regarding institutional support, and output factors such as knowledge gained. A survey of 322 online instructors identified that: (1) Instructors from Chinese and Mexican universities saw student characteristics as most important. (2) Instructors from an American university perceived institutional support as key. (3) Instructors from a Spanish university reported that learning outcomes mattered most. In general, instructors valued course content, social interaction, instruction quality over technological issues.
Creating Effective Pediatric Assent Forms: Overcoming Common Obstaclesjbarag
This document discusses creating effective pediatric assent forms by overcoming common obstacles. It identifies five main obstacles: 1) treating assent as an afterthought, 2) lack of direction from sponsors/IRBs, 3) failure to account for developmental ages and reading levels, 4) difficulty creating readable forms, and 5) not planning the assent process logistics. It provides tools to write forms at appropriate reading levels, ensure all elements of assent are addressed, and plan who will obtain assent and where. The goal is to engage children in a developmentally-appropriate way and respect their participation in research decisions.
The document discusses Builders FirstSource's non-GAAP financial measures and forward-looking statements. It provides cautionary language regarding forward-looking statements, noting that actual results could differ materially from projections. It also notes that non-GAAP measures should be considered in addition to GAAP measures but should not be considered substitutes for GAAP results. Reconciliation tables are included to reconcile non-GAAP measures to their GAAP equivalents.
This document is the 2014 annual report for Girl Scouts of South Carolina Mountains to Midlands. It summarizes the organization's 2014 programming, leadership awards earned, and travel destinations attended by local Girl Scouts. While membership declined slightly from the previous year, the organization remained focused on providing high-quality leadership development opportunities for girls through programming in areas like STEM, literacy, and outdoor activities. Key events included piloting a new literacy program and expanding engineering-focused badge options. The report highlights the 22 Girl Scouts who earned the Gold Award and over 95 who earned the Silver Award for their leadership projects.
This curriculum vitae summarizes the qualifications and experience of Syed Mohd Ilyas Bin Syed Mohd Nassir. He has 10 months of experience as a plant engineer and is seeking a salary increase to MYR 2,800. He is leaving his current role to expand his technical knowledge and work on larger projects. He has a Bachelor's in Mechanical Engineering with honors from Universiti Tenaga Nasional and relevant experience in areas like biodiesel plant design, maintenance, and safety assessments.
12월 5주차 주간 인기랭킹(2012.12. 31. ~01. 06.)
공식페이지: https://www.facebook.com/calcuttac
한 주간 애플 앱스토어 인기 앱을 소개합니다.
이번주 최고의 인기앱은 “컴투스 홈런왕 for Kakao (개발사: Com2uS Inc.)”으로 4계단 상승하여 아이폰 무료 앱 1위를 차지했습니다. “FIFA 13 by EA SPORTS (개발사: Electronic Arts $0.99)가 변동 없이 아이폰 유료 앱 부분에서 1위 자리를 지켰습니다.
[아이패드 앱 부분]
“YouTube (개발사: Google, Inc)”이 2계단 상승하여 iPad 무료 앱 부문에서 1위를 차지했고, FIFA 13 by EA SPORTS (개발사: Electronic Arts $0.99)” 가 변동 없이 iPad 유료 앱 부문에서 1위 자리에 올랐습니다.
[금주의 앱 배틀 : 생산성부분 메모의 최강 아이패드 앱배틀! UPAD VS 어썸노트 ]
한주간 생산성부분 인기최고의 아이폰부분 유료앱은 “Clear($1.99)”라는 앱으로 할일을 체크하여 말끔하게 정리해주는 체크리스트 싱천용 앱입니다. 총 16개 국가에서 1위의 자리를 차지하고 있습니다.
아이패드 유료앱중에서 메모기능으로 강력한 유명한 두가지 앱의 애틀을 소개합니다. UPAD와 어썸노트HD의 인기는 한국은 물론 세계적으로 잘나가는 인기앱입니다. 메모 기본의 기능은 물론 사진과 함께 편집이 쉽고 실제 노트의 UI를 반영한 매우 편리한 노트앱입니다. 유료 구매를 하셔도 절대 후회하지 않을 앱이라고 자부합니다.
Gamification Strategies in a Hybrid Exemplary College CourseSzymon Machajewski
Using technology in teaching and learning finds a wide adoption in recent years. 63.3% of chief academic leaders surveyed by the Babson Survey Research Group confirm that online education is critical to their long-term strategy. Modern engagement pedagogies, such as digital gamification, hold a promise of shaping student experience. While course builders and instructors investigate new technologies and teaching methods questions arise about the instructional quality of academic courses with online content or with gamification elements. In addition, students are not the digital natives many hoped them to be. 83% of millennials report sleeping with their smartphones, but 58% have poor skills in solving problems with technology. This paper reports on a gamefully designed course, delivered in a hybrid modality, which was selected through a peer review process as an exemplary course in consideration of instructional design. The course was evaluated according to the Blackboard Exemplary Course Program rubric. Gamification was introduced in three phases: player onboarding phase, player scaffolding phase, and player endgame. Various technologies involved in the course included: MyGame gamification mobile app, Blackboard Learn, Cengage Skills Assessment Manager, Kahoot, Amazon Alexa, Google Traveler, Twitter, and others. The course focused on gamification according to the short and long game theory to engage students during lectures (short game) and throughout the semester (long game).
Faculty attitudes towards integrating technology and innovationIJITE
Technological innovation is an important aspect of teaching and learning in the 21st century. This article
examines faculty attitudes toward technology use in the classroom at one regional public university in the
United States. Building on a faculty-led initiative to develop a Community of Practice for improving
education, this study used a mixed-method approach of a faculty-developed, electronic survey to assess this
topic. Findings from 72 faculty members revealed an overall positive stance toward technology in the
classroom and the average faculty member utilized about six technology tools in their courses. The
opportunities, barriers and future uses for technologies in the higher education classroom emerged from
the open-ended questions on the survey. One finding of particular concern is that faculty are fearful that
technology causes a loss of the humanistic perspective in education. The university is redesigning ten of its
most popular courses to increase flexibility, accessibility and student success.
Ziyanak, sebahattin the effectiveness of survey instruments nfaerj v29 n3 2016William Kritsonis
This article examines how sociological imagination of the individuals living in southeastern Turkey is constructed through Movie, The Bliss. Traditional and modern forms of life are symbolically constructed in this movie. The framework of “honor killing,” “masculinity in southeastern Turkey," “cultural deficiency,” and “othering” will be analyzed to explicate how stereotypical southeastern characters are reproduced. Content analysis technique is applied to interpret apparent and latent contents, contexts, aspects and so forth. Developed categories are revisited through Ibn Khaldun's Typology, cultural deficiency theory, Tonnies’ theory, Durkheim’s view on society, and Goffman’s framing process.
William Allan Kritsonis, PhD - Editor-in-Chief, NATIONAL FORUM JOURNALS (Established 1982)
This document provides an overview of Russell Francis's doctoral thesis investigating the implications of media change for learning. The thesis explored how learners are appropriating new media to support learning through two design experiments and an ethnographic study of advanced learners. The ethnographic study identified six genres of practice used by learners and attempted to develop conceptual tools to understand new media literacies. The thesis argues that media convergence has decentralized formal education and learners now need new media literacies to design personal learning environments leveraging online resources.
Administrative Support of Faculty Preparation and Interactivity in Online Tea...William Kritsonis
Administrative Support of Faculty Preparation and Interactivity in Online Teaching: Factors in Student Success by Dr. Jon E. Travis and Grace Rutherford - Published in NATIONAL FORUM JOURNALS - www.nationalforum.com - Dr. William Allan Kritsonis, Editor-in-Chief
This document summarizes a study on how first year students at Amsterdam University of Applied Sciences use Facebook. Previous research identified four major Facebook activities: information sharing, sharing for educational purposes, social purposes, and leisure. The study surveyed 618 first year students about their Facebook activities and background characteristics. The survey results were analyzed using statistical software to determine if certain Facebook activities correlated with student characteristics like gender, age, or socioeconomic status. Understanding how students use Facebook could provide insights into its relationship with student success in higher education.
Dr. Diane Hamilton explores the relationship between media choices and teach experience in online courses. This presentation won best award at the CSI conference.
Education 4.0 and Computer Science: A European perspectiveBart Rienties
This systematic literature review aimed at identifying the pedagogical approaches, aligned with Education 4.0, used to support teaching computer science courses with undergraduate and graduate students in Europe. A three-step coding process was conducted to identify and analyse 20 papers. Quantitative and qualitative analysis of the selected papers revealed a three-cluster solution with common characteristics that could be used to describe those pedagogical approaches. The review also showed that the term Education 4.0 is still relatively new and has not been conceptualised in terms of computer science courses, although the characteristics of Education 4.0 are visible throughout the pedagogical approaches.
Bart Rienties, Rebecca Ferguson, Christothea Herodotou, Francisco Iniesto, Julia Sargent, Igor Balaban, Henry Muccini, Sirje Virkus
This document discusses a study that examined faculty involvement in blended instruction and their attitudes towards it at universities. The study surveyed coordinators and faculty at extensive doctoral research universities. It found that the most common blended method was supplementary online materials for face-to-face classes. Faculty were actively involved in designing online content and had positive attitudes towards blended instruction. Universities provided support like online help desks but faced challenges from high faculty workloads and lack of motivation to adopt blended models.
University students were surveyed about their use of digital technologies for self-regulated learning. The results showed that:
1) Students frequently use the internet, communication tools, and repositories to search for information and exchange social support, but tend not to use technologies to regulate their own learning process.
2) The most common self-regulated learning strategies used with technology were related to social support, such as sharing materials and commenting on information.
3) Two distinct groups of students were identified that differed in their use of self-regulated learning strategies with technology, with some groups making more use of these strategies than others.
Cognitive Communication 2.0 in the Classroom - Resonance of an Experience in ...Cornélia Castro
This document summarizes a study that evaluated the integration of Twitter into PowerPoint presentations in university classrooms. The study aimed to assess how this approach could transform one-way communication between the teacher and many students (cognitive communication 1.0) into more interactive communication between all participants (cognitive communication 2.0). A questionnaire was administered to 122 university students after they experienced a PowerPoint presentation integrated with Twitter in their classroom. The questionnaire assessed the approach across several dimensions related to pedagogy, technology, learning, classroom interactions, and behaviors. Preliminary results found that students recognized the pedagogical and technological benefits of the approach and perceived positive effects on the quality of their learning and interactions in the classroom.
Oumh1103 pdf factor influencing adult learners' decicion to drop out or per...Mohamad Kelana Mat
1) The study examines factors that influence adult learners' decisions to drop out of or persist in online courses, comparing 147 learners who dropped out versus those who completed courses.
2) It finds that dropouts and completers differ significantly in their perceptions of family and organizational support as well as their satisfaction and perception of course relevance.
3) A theoretical framework including individual characteristics, family/organizational support, satisfaction, and relevance can predict whether learners will drop out or persist, with organizational support and relevance being particularly predictive. The results suggest lower dropout rates may result from enhancing course relevance and support for learners.
A cross-national study of teacher’s perceptions of online -2016.pdfJackeline Ferreira
This study examines perceptions of success factors in online learning from instructors' perspectives across four universities in different countries. It uses a model that considers input factors related to students, process factors regarding institutional support, and output factors such as knowledge gained. A survey of 322 online instructors identified that: (1) Instructors from Chinese and Mexican universities saw student characteristics as most important. (2) Instructors from an American university perceived institutional support as key. (3) Instructors from a Spanish university reported that learning outcomes mattered most. In general, instructors valued course content, social interaction, instruction quality over technological issues.
Creating Effective Pediatric Assent Forms: Overcoming Common Obstaclesjbarag
This document discusses creating effective pediatric assent forms by overcoming common obstacles. It identifies five main obstacles: 1) treating assent as an afterthought, 2) lack of direction from sponsors/IRBs, 3) failure to account for developmental ages and reading levels, 4) difficulty creating readable forms, and 5) not planning the assent process logistics. It provides tools to write forms at appropriate reading levels, ensure all elements of assent are addressed, and plan who will obtain assent and where. The goal is to engage children in a developmentally-appropriate way and respect their participation in research decisions.
The document discusses Builders FirstSource's non-GAAP financial measures and forward-looking statements. It provides cautionary language regarding forward-looking statements, noting that actual results could differ materially from projections. It also notes that non-GAAP measures should be considered in addition to GAAP measures but should not be considered substitutes for GAAP results. Reconciliation tables are included to reconcile non-GAAP measures to their GAAP equivalents.
This document is the 2014 annual report for Girl Scouts of South Carolina Mountains to Midlands. It summarizes the organization's 2014 programming, leadership awards earned, and travel destinations attended by local Girl Scouts. While membership declined slightly from the previous year, the organization remained focused on providing high-quality leadership development opportunities for girls through programming in areas like STEM, literacy, and outdoor activities. Key events included piloting a new literacy program and expanding engineering-focused badge options. The report highlights the 22 Girl Scouts who earned the Gold Award and over 95 who earned the Silver Award for their leadership projects.
This curriculum vitae summarizes the qualifications and experience of Syed Mohd Ilyas Bin Syed Mohd Nassir. He has 10 months of experience as a plant engineer and is seeking a salary increase to MYR 2,800. He is leaving his current role to expand his technical knowledge and work on larger projects. He has a Bachelor's in Mechanical Engineering with honors from Universiti Tenaga Nasional and relevant experience in areas like biodiesel plant design, maintenance, and safety assessments.
12월 5주차 주간 인기랭킹(2012.12. 31. ~01. 06.)
공식페이지: https://www.facebook.com/calcuttac
한 주간 애플 앱스토어 인기 앱을 소개합니다.
이번주 최고의 인기앱은 “컴투스 홈런왕 for Kakao (개발사: Com2uS Inc.)”으로 4계단 상승하여 아이폰 무료 앱 1위를 차지했습니다. “FIFA 13 by EA SPORTS (개발사: Electronic Arts $0.99)가 변동 없이 아이폰 유료 앱 부분에서 1위 자리를 지켰습니다.
[아이패드 앱 부분]
“YouTube (개발사: Google, Inc)”이 2계단 상승하여 iPad 무료 앱 부문에서 1위를 차지했고, FIFA 13 by EA SPORTS (개발사: Electronic Arts $0.99)” 가 변동 없이 iPad 유료 앱 부문에서 1위 자리에 올랐습니다.
[금주의 앱 배틀 : 생산성부분 메모의 최강 아이패드 앱배틀! UPAD VS 어썸노트 ]
한주간 생산성부분 인기최고의 아이폰부분 유료앱은 “Clear($1.99)”라는 앱으로 할일을 체크하여 말끔하게 정리해주는 체크리스트 싱천용 앱입니다. 총 16개 국가에서 1위의 자리를 차지하고 있습니다.
아이패드 유료앱중에서 메모기능으로 강력한 유명한 두가지 앱의 애틀을 소개합니다. UPAD와 어썸노트HD의 인기는 한국은 물론 세계적으로 잘나가는 인기앱입니다. 메모 기본의 기능은 물론 사진과 함께 편집이 쉽고 실제 노트의 UI를 반영한 매우 편리한 노트앱입니다. 유료 구매를 하셔도 절대 후회하지 않을 앱이라고 자부합니다.
The document provides guidance for journalists on using Twitter to improve journalism, connect with communities, and change newsroom culture. It discusses how Twitter can help locate sources, monitor discussions, and promote content. The document then gives specific tips for journalists on setting up an account, following others, tweeting, using hashtags and retweets, and ethical considerations of using Twitter. It also provides lists of journalists, editors, bloggers, academics, and locals that would be good for other journalists to follow on Twitter.
The document discusses Builders FirstSource's non-GAAP financial measures and forward-looking statements, provides an overview of the company and industry, and highlights the company's strategic growth plan, fully integrated distribution platform, product and service offerings, experienced management team, and financial performance in 2011 with an 11.2% increase in sales and 150 basis point increase in gross margins.
This document provides an overview of bitcoin and its key features through a series of images and captions. It discusses what bitcoin is, how it works as a digital currency using blockchain technology, the process of mining new bitcoins through solving cryptographic puzzles, and some of bitcoin's claimed advantages over traditional currencies such as its decentralized nature and lack of government involvement. It also touches on criticisms of bitcoin relating to speculative bubbles, Ponzi schemes, and similarities to historical financial crises.
Christine Baker founded Walk4Good to inspire people to practice kindness. This presentation, first made to Vaughn College, highlights Walk4Good and how institutions from the inside out can create a culture of kindness.
Triptofanito Julio Frenk el viaje por el cuerpo humanoBiologia Epo
Nuestra historia comienza en una granja. Esta granja era muy hermosa. En ella había verduras, frutas, vacas, toros y muchas, pero muchas, gallinas. Además, en la granja vivía un granjero. El comía las verduras, las frutas, la leche, la carne y los huevos que se producían en su granja.
Gracias a esto el granjero vivía como un rey. Aquí dejamos nosotros a Triptofanito y Lisina. Estamos seguros de
que ellos podrán llevar a cabo sus hermosos sueños. Pero esta historia no ha terminado aún. Recuerden siempre, amigos lectores, que cada vez que coman estarán llevando a su organismo unos nuevos aminoácidos. Unos aminoácidos que estarán siempre dispuestos a ser sus amigos, siempre dispuestos a entregarles algo, siempre dispuestos a realizar un nuevo viaje por el Cuerpo Humano.
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The document is a message from Ps Phua Seng Tiong for the Sungai Way-Subang Methodist Church on November 1, 2015. It discusses the importance of having a relationship with God, reflecting on scripture together, and responding to God's call through serving together as the church. Key verses cited encourage being strong in faith through knowing God and using all abilities to do God's work.
The document provides details for a March holidays camp for the Raffles Institution Chinese Orchestra. It includes an overview of the camp schedule and activities such as team-bonding games, an oral quiz, and an Amazing Race competition between groups. The schedule outlines the timeline and locations for each activity. The document also lists the group allocations and objectives to promote bonding between members. Descriptions are provided for each of the main activities, including instructions for facilitators. The goal of the camp is to build unity and a sense of belonging within the orchestra.
This resume summarizes the qualifications and experience of Muhammad Salahuddin, a Pakistani nurse seeking a career in a reputable healthcare organization. He has over 20 years of experience working in critical care units in Pakistan and the UAE. His experience includes working as a senior nurse, head nurse, and intensive care technician. He has extensive skills in caring for patients on ventilators, managing cardiac patients, and overseeing nursing staff. He is licensed in the UAE and has numerous certifications in critical care nursing.
Lịch sử phát triển Web
2. Lý do ra đời của ASP.NET MVC
2.1 Giới thiệu ASP.NET truyền thống
2.2 Nhược điểm ASP.NET truyền thống
2.3 Giới thiệu ASP.NET MVC (model-view-controller)
2.3.1 Nguồn gốc ASP.NET MVC
2.3.2 Các thành phần cấu thành ASP.NET MVC
2.3.3 Cấu trúc mặc định của một dự án ASP.NET MVC
2.4 So sánh giữa ASP.NET và ASP.NET MVC
2.5 MVC2
3. Tìm hiểu các thành phần bên trong ASP.NET MVC
3.1 Controllers và Actions
3.1.1 Controllers là gì ?
3.1.2 Controller Actions là gì ?
3.2 Views
3.2.1 Views là gì ?
3.2.2 Tạo Views như thế nào ?
3.2.2 Sử dụng Views như thế nào ?
3.3 Models
3.3.1 Models là gì ?
3.3.2 Tạo Database
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The document outlines Avon's strategic management case study, including an analysis of Avon's internal strengths and weaknesses as well as external opportunities and threats, and recommends potential strategies for Avon such as expanding into key Asian and African markets to pursue growth opportunities, building their brand image among Generation Y consumers in North America, and focusing on innovations in their core beauty products.
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This document discusses a study on the impact of MOOCs (Massive Open Online Courses) in Indian higher education. It begins by providing background on the development of MOOCs and their potential benefits, including increasing access to education. It then discusses the goals of institutions that offer MOOCs, such as increasing their reach and brand, lowering costs, and conducting research. The document also analyzes data on current MOOC users, finding they are often already educated and wealthy. It concludes by discussing how MOOCs could help address faculty shortages and improve education quality in India by providing online courses.
Annoted bibliography· Alraimi, K. M., Zo, H., & Ciganek, A. .docxjustine1simpson78276
Annoted bibliography
· Alraimi, K. M., Zo, H., & Ciganek, A. P. (2015). Understanding the MOOCs continuance: The role of openness and reputation. Computers & Education, 80, 28-38.
In this paper the authors’ states that Massive Open Online Courses (MOOCs) are classes conveyed in an online domain with a few highlights that are not the same as past ways to deal with online training. The viability of MOOCs is an open inquiry as finish rates are considerably not as much as customary online training courses. The goal of this investigation is to recognize factors that improve an individual' aim to keep utilizing MOOCs, in which a restricted measure of research has beforehand investigated. An examination demonstrates in light of the data frameworks duration desire affirmation display is proposed and tried with information gathered at an extensive scale previously researched. The research demonstrates clarified a considerable level of the change for the aim to keep utilizing MOOCs, which is essentially affected by seen notoriety, saw receptiveness, saw helpfulness, and client fulfilment. Seen notoriety and saw receptiveness were the most grounded indicators and have not beforehand been analyzed with regards to MOOCs. When massive open online courses or MOOCs first appeared in 2008 and more widely spread by global MOOC providers in 2011-2012, they were predicted to achieve world domination and transformation of higher education. Today, these predictions are seen to have been overblown. But with several years of experience now behind them, MOOC providers and users are adjusting both their perceptions about online learning and the courses themselves. A review of the literature was undertaken to develop a better understanding of the evolution and ramifications of MOOCs and the lessons learned so far about their role in and contributions to teaching and learning. Results showed that MOOC learners are diverse, are from many countries across the globe and are of all cultural backgrounds and all ages, and that they want to gain both educational and career benefits from taking MOOCs. Prior knowledge with a subject and early and consistent engagement appear to be important for the success in MOOC learning. Unfortunately no evidence was found to support the role of MOOCs in mitigating gender-, location-and generation-disparities in higher education. The paper concluded with discussion of the emerging issues and implications for higher education.
· Burd, E. L., Smith, S. P., & Reisman, S. (2015). Exploring business models for MOOCs in higher education. Innovative Higher Education, 40(1), 37-49.
In this research article the writer Burd, Smith and Reisman explores the possibilities of different business models in higher education through MOOC. Massive Open Online Courses (MOOCs) conceivably challenge the conventional strength of physical establishments as suppliers of value advanced education. The advantages of understudies incorporate decreased train.
Knowledge, social media and technologies for a learning societywanzahirah
The document summarizes several papers presented in a special issue of the journal Transactions of the SDPS on the topics of knowledge, social media, and technologies for learning. The papers explore how new technologies and social media are changing learning and discuss approaches like using smartphones and scaffolding tools to enhance the learning process. They also address challenges in recommending learning resources and the role of collective intelligence in driving innovation. The goal of the special issue is to look at the future of education from a transdisciplinary perspective.
Disruptive Innovation in Education: Open Learning, Online Learning, MOOCs and...inventionjournals
Rapid advancement of information and communication technologies has brought about various changes in education and the structure of colleges and universities. The education system is undergoing dynamic changes. These dynamic changes can be explained from the perspective of theory of disruptive innovation which basically explained that existing organizations were displaced from their industries because of their inertia towards the adoption of new innovation. Recent introduction of massive open online courses (MOOCs) is seen as a strong technological force influencing the education landscape. MOOCs which are operated on a different scale and expectation have triggered existing education providers to evaluate and strategize in order to remain relevant. This study is meant to give readers, especially practitioners in education sector a way of understanding the nature of disruptive innovation and how disruptive innovation is influencing the education landscape
Identifying students’ profiles for MOOCs – a social media analysisGabriela Grosseck
This document analyzes survey responses from 632 students in Romania about their participation in Massive Open Online Courses (MOOCs). The author uses complex network analysis to model the students as a graph based on their shared traits and reasons for engaging with MOOCs. Six distinct profiles of students who participate in MOOCs are identified from the graph's community structure. The profiles differ based on gender and traits like certification goals, recognition priorities, and motivation types. The analysis provides insight into how students relate to online education and how MOOC design could be optimized for different profiles.
MOOCs have grown significantly since first emerging in 2008 but completion rates remain low at 4-5%. While MOOCs were initially seen as democratizing education, studies show the primary users are already affluent and educated. However, MOOCs have evolved from simple online lectures to more interactive experiences and provide benefits to some students in developing countries or those seeking to bolster skills. One popular MOOC on superheroes explores their history and impact in pop culture through a collaboration between edX and the Smithsonian Institution.
This document discusses Massive Open Online Courses (MOOCs). It defines MOOCs and traces their development from early open online courses in the 2000s to present-day commercial platforms with millions of students. The document outlines the key characteristics of MOOCs, including their open access, unlimited participation, and use of online forums to build communities. It also categorizes different types of MOOCs and discusses their benefits as well as potential downsides.
This document discusses Massive Open Online Courses (MOOCs). It defines MOOCs and traces their evolution from early open learning models to their current form. The document outlines the key characteristics of MOOCs, including that they are open to unlimited participation, have no formal entry requirements, and use interactive online forums. It also describes the main types of MOOCs and discusses some of the benefits and downsides of taking MOOCs.
This document summarizes a study that investigated instructors' and learners' attitudes toward e-learning. Surveys were administered to 37 instructors and 105 learners at a university to collect data on their technology experience and attitudes toward e-learning. The surveys included questions about experience with technologies and Likert scale responses to statements about e-learning attitudes. Results from both groups were analyzed independently and compared to examine relationships between experience and attitudes. The study aimed to provide insight into factors that influence perspectives on e-learning.
Advancement In Massive Open Online Courses (MOOCs) To Revolutionize Disruptiv...Jody Sullivan
This document summarizes research on students' awareness and perceptions of Massive Open Online Courses (MOOCs) in Pakistan. Interviews were conducted with 24 students, 12 who had taken MOOC courses and 12 who had not. The findings showed that students with no MOOC experience had minimal awareness of such programs and their potential benefits, which included knowledge gain, skills development, professional development, learning opportunities, employability, and usefulness for their jobs. Experts on distance education were also interviewed using the Delphi method. The research uncovered that students' inclination towards MOOCs in Pakistan is still in the early stages.
WHY DO LEARNERS CHOOSE ONLINE LEARNING THE LEARNERS’ VOI.docxmansonagnus
WHY DO LEARNERS CHOOSE ONLINE LEARNING:
THE LEARNERS’ VOICES
Hale Ilgaz and Yasemin Gulbahar
Ankara University, Distance Education Center, 06830 Golbasi, Ankara, Turkey
ABSTRACT
Offering many advantages to adult learners, e-Learning is now being recognized - and preferred - by more and more
people, resulting in an increased number of distance learners in recent years. Numerous research studies focus on learner
preferences for online learning, with most converging around the individual characteristics and differences, if not the
features of the technology and pedagogy used. For Turkey, the situation is also similar, with the number of adult learners
who prefer online learning increasing each year due to several reasons. The result of this is an increase in the number of
online programs offered by many universities. Hence, this research study has been conducted to reveal the prevailing
factors causing learners to choose online learning. Through this qualitative research regarding online learners in a state
university, it is found that having a full time job, accessibility and flexibility, individual responsibility, effective time
management, physical distance, institutional prestige, disability are the common factors for under graduate and graduate
learners in their preference for online learning. Awareness of these factors can support the stakeholders while designing
e-Learning from both technological and pedagogical points of view.
KEYWORDS
Online learning, preferences, expectations
1. INTRODUCTION
Offering many advantages to adult learners, e-Learning is now being recognized - and preferred - by more
and more people, resulting in an increased number of distance learners in recent years. Emphasizing that
distance education has a bright and promising future, Zawacki-Richter and Naidu (2016) stress that, “In fact,
there has never been a better time to be in the field of open, flexible, distance and online education than
now!” (p. 20).
The commonly discussed factors that make online learning attractive for adults are: independence from
time and place; accessibility, and; economic reasons. With the MOOC movement, extremely high quality
online courses are now being delivered to learners by many well-known universities. Moreover, many
universities are either providing online programs or courses as a support to traditional instruction, in the form
of blended learning, flipped classes, etc. Indeed, there are almost no universities left who don’t benefit from
these advantages of technology usage and its support in teaching-learning processes.
A variety of reasons might account for these learning preferences. Çağlar and Turgut (2014) attempted to
identify the effective factors for the e-learning preferences of university students; they concluded that,
“Efficient usage of time and reduced educational expenses were found to be on top of the list as the most
valued advantages of e-learning” (p. 46). ...
Monitoring Academic Performance Based on Learning Analytics and Ontology: A S...Tiago Nogueira
This document presents a systematic literature review that analyzed 31 studies on how learning analytics and ontologies can help monitor academic performance based on taxonomies of educational objectives. The review identified four main findings: 1) Few studies consider how student interactions in learning management systems represent learning experiences; 2) Most studies use ontologies to assess learning objects and enable sequencing; 3) No methods were identified that evaluate academic performance guided by taxonomies; 4) No studies coordinated the use of learning analytics and ontologies for academic performance monitoring. The review concludes there is a need for future research proposals on new models for evaluating academic performance.
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From sMOOC to tMOOC, learning towards professional transference: ECO European Project De sMOOC a tMOOC, el aprendizaje hacia la transferencia profesional: El proyecto europeo ECO
Activities And Resources In Online Learning From A Critical Thinking ViewSamantha Martinez
This document summarizes a study that investigated the relationship between online learning and critical thinking in an undergraduate online introductory computer literacy course. The study aimed to identify which parts of the course's learning modules (resources and activities) contributed most to developing students' critical thinking skills. A survey of 490 students found that interactive course components like assignments and projects were perceived to contribute more to critical thinking than static resources like textbooks. Assignments had the strongest perceived impact, followed by an Excel mini case project. Overall, results indicated students viewed interactive elements as contributing more to critical thinking development than non-interactive content, assessment, or projects. The findings suggest online course designers should focus on incorporating interactive elements to foster critical thinking skills.
Read the article Adult Education and the Social Media Revolution,.docxmakdul
Read the article “Adult Education and the Social Media Revolution,” available in the eReserves section of the classroom. Pay particular attention to the references these authors make to the works of others. Every citation within this article is essentially a head nod to other authors who have written about the same or similar topics. Were they all in the same room, you could imagine the authors of this article pointing to or calling out those other authors while speaking. This is what we mean when we refer to research and writing as one big conversation, with all of the participants listening and responding to one another.
In a discussion post, point to an example from this article and explain how the authors do one of the following:
· refer to another work in order to give legitimacy to their own point;
· refer to another work in order to build upon the ideas of others; or
· refer to another work in order to challenge that work.
If you select "refer to another work in order to give legitimacy to their own point," first describe what the authors' point is, then describe how the cited article supports that point.
If you select "refer to another work in order to build upon the ideas of others," first describe what the ideas are, then describe how the authors build upon those ideas.
If you select "refer to another work in order to challenge that work", first describe what is being challenged, then describe how the authors are challenging the cited work.
By Marvin LeNoue, Tom Hall,
Myron A. Eighmy
Marvin LeNoue is an ABD doctoral
candidate in Occupational and Adult
Education at North Dakota State
University, Fargo, ND. He is currently
serving as an instructor at the University
of Oregon American English Institute,
Eugene, OR. His research interests
include technology-enhanced education
delivery and the use of educational
social software.
(Email: [email protected])
Tom Hall has an Ed. D. in Adult and
Higher Education from the University
of South Dakota. He is currently
serving as an Assistant Professor in the
Educational Leadership Program at
North Dakota State University, Fargo,
ND. His research interests include
adult education in the 21st Century, the
impact of different generational cohorts
in today's workplace, and community
education in rural America.
(Email: thomas.e. [email protected] edu)
Myron A. Eighmy is a professor and
program coordinator for the Education
Doctoral Program at North Dakota State
University. Research interests include
alternative delivery modes, learning
communities, and graduate student
self-efficacy.
(Email: [email protected])
Adult Education and the
Social Media Revolution
The advent of Web 2.0 and the spread of social software tools havecreated new and exciting opportunities for designers of digitally-medi-
ated education programs for adults. Whether working in fully online, blended,
or face-to-face learning contexts, instructors may now access technologies that
allow students and faculty to engage in coope ...
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Mooc user experience research paper
1. Thirty Fourth International Conference on Information Systems, Milan 2013 1
WHAT MAKES A GREAT MOOC? AN
INTERDISCIPLINARY ANALYSIS OF STUDENT
RETENTION IN ONLINE COURSES
Completed Research Paper
Panagiotis Adamopoulos
Department of Information, Operations and Management Sciences
Leonard N. Stern School of Business, New York University
44 W 4th St, New York, NY 10012
padamopo@stern.nyu.edu
Abstract
Massive Open Online Courses (MOOCs) have experienced rapid expansion and gained
significant popularity among students and educators. Although the broad acceptance of
MOOCs, there is still a long way to go in terms of satisfaction of students’ needs, as
witnessed in the extremely high drop-out rates. Working toward improving MOOCs, we
employ the Grounded Theory Method (GTM) in a quantitative study and explore this
new phenomenon. In particular, we present a novel analysis using a real-world data set
with user-generated online reviews, where we both identify the student, course,
platform, and university characteristics that affect student retention and estimate their
relative effect. In the conducted analysis, we integrate econometric, text mining, opinion
mining, and machine learning techniques, building both explanatory and predictive
models, toward a more complete analysis. This study also provides actionable insights
for MOOCs and education, in general, and contributes to the related literature
discovering new findings.
Keywords: Massive Open Online Courses, MOOCs, online learning, student retention,
grounded theory method, econometric analysis, choice models, text mining, opinion
mining, predictive modeling, user-generated content, user satisfaction, utility
2. Breakthrough Ideas in IS
2 Thirty Fourth International Conference on Information Systems, Milan 2013
Introduction
Massive Open Online Courses (MOOCs) are a recent development in the area of e-learning and distant
education that gains significant popularity among both students and educators. MOOCs are larger in scale
than traditional courses, have no restrictions on individual participation, they are globally distributed
across a variety of networks, and aim at revolutionizing the way education happens. Such massive online
courses are offered in a wide range of topics, spanning the Humanities, Social Sciences, Mathematics,
Engineering, Computer Science, and other disciplines. Besides, dozens of top universities, including many
international and Ivy League institutions, are offering a large number of MOOCs. The aforementioned
factors, together with the open nature of the courses and the lack of need for physical presence, attract a
huge number of students from a wide variety of backgrounds. Interestingly, some prominent MOOCs,
such as various classes from Stanford University, edX, Coursera, and Udacity, have attracted tens of
thousands of participants. For instance, as of November 2012 more than 1,900,241 students from 196
countries have enrolled in at least one course by Coursera.
Even though MOOCs have been broadly accepted, there is still plenty of room for improvement as far as
the actual needs of students are considered. This is evident if we take into consideration that the student
retention rates are very low (Clow 2013; Downes 2010; Knowledge@Wharton 2013; Lewin 2013). At the
same time, the huge popularity in starting students shows that the time is ripe for MOOCs (Fox and
Patterson 2012; Pappano 2012). Nevertheless, it is the duty of the academic community to shed light on
the problems of MOOCs, trying to both understand their causes and provide actionable solutions, in order
open education to achieve its potential and not fail. Working toward this direction, we explore one of the
most important, if not the most important, problems of MOOCs. In this study, we develop a hedonic-like
approach to identify important concepts that affect online course retention and estimate their relative
importance based on textual reviews submitted by students on special online communities. In particular,
we employ the Grounded Theory Method (GTM) (Eisenhardt 1989; Glaser and Strauss 1967) on
quantitative data, a less frequently applied paradigm, which provides a flexible way of conducting
research that prioritizes exploration of the given phenomenon in a predominantly inductive theory
development paradigm (Birks et al. 2013) in Information Systems (IS). Overall, we use radically different
approaches and interdisciplinary perspectives to provide a holistic and deep view of a novel phenomenon
of scientific and societal interest. Our goal is to understand the behavioral process that leads to the
students’ choices and, thus, we take a causal perspective since there are factors that collectively determine,
or cause, the agents’ choices (Train 2003). In addition, we aim at contributing to the related literature and
improving course design and, thus, online and distance education. Finally, we hope to inspire future
research and open up new streams of scientific inquiry for the IS field.
In this paper, we identify the important determinants that affect retention in online courses and conduct
an innovative analysis to study their relative importance. We employ GTM in a quantitative study that
integrates econometric, text mining, opinion mining, and predictive modeling techniques toward a more
complete analysis of the information captured by user-generated content. Additionally, we provide
actionable insights with important implications for both MOOCs and education, in general, and
contribute to the related literature on student retention, course design, and educational policy by
discovering new findings.
Related Work
While Massive Open Online Courses (MOOCs) are a relatively recent development, the ideas behind
MOOCs can be traced as back as the early 1960s, when Fuller (1962) proposed industrial scale educational
technology. In particular, MOOCs succeeded the trend of Open Education movement that focuses on how
open education tools, resources, and knowledge can improve the quality of education (Iiyoshi and Kumar
2008). The open education tools in combination with the latest technological advancements allow
professors and educational institutions to offer MOOCs with massive number of students (Fox and
Patterson 2012) and, at the same time, they allow students to overcome geographic and economic barriers
(Russell et al. 2013) and pursue their own learning objectives (Armstrong 2012). From the beginning of
such developments, many practitioners and researchers, such as Harasim et al. (1995), have put great
emphasis on the use of learning networks for knowledge construction, discussing how people learn in
3. Adamopoulos / What Makes a Great MOOC?
Thirty Fourth International Conference on Information Systems, Milan 2013 3
large open networks which offer extensive diversity and connectivity (Mackness et al. 2010). Exploring
different dimensions of MOOCs, Cormier and Siemens (2010) and Masters (2011) discussed how the roles
of the professor (instructor) have changed, which now include amplifying, curating, aggregating, filtering
or selecting, modeling, and staying present, while Xu and Jaggars (2013) examined the extent to which
students perform as well online as they do face-to-face, and Mak et al. (2010) studied the importance of
blogs and discussion forums as communication and learning tools in a MOOC. In other streams of
research, Joglekar et al. (2013) and Singh et al. (2013) proposed methods for automatically providing
feedback and evaluating the assignments of students, Piech et al. (2013) developed algorithms for
estimating and correcting for grader biases and reliabilities in peer assessment in MOOCs, and Sadigh et
al. (2012) discussed automated exercise generation. In addition, regarding the retention problem,
Cormier and Siemens (2010) and Russell et al. (2013) asked from where the key value of a course in the
educational systems comes and Mackness et al. (2010) posed the question of how to design a course which
will provide participants with positive experiences. Toward this direction, Vihavainen et al. (2012)
recently established a positive statistical significant correlation between the difficulty of a MOOC and its
educational value. Nevertheless, despite the wide scientific interest, much more work should be done to
improve MOOCs. In particular, researchers should shed light on the problems of MOOCs and try to solve
the problem of student retention as witnessed in the high drop-out rates (Clow 2013; Lewin 2013).
However, the problem of student retention is not pertaining only to MOOCs. In traditional education, the
first national retention study in the United States surveyed 25 universities and reported a dropout rate of
45% (MacNeely 1938). Examining this phenomenon, Tinto (1975; 1987) proposed the student integration
model to study college dropout and explain the process that motivates individuals to leave colleges and
universities before graduating while Bean (1985) proposed the student attrition model to study the
conscious, openly discussed student intention to leave an institution, coupled with actual attrition,
emphasizing academic, social, and personal outcomes of the selection or socialization of students at an
institution. Besides, Avakian et al. (1982) studied race and sex differences in student retention at an urban
university, Boudreau and Kromrey (1994) examined the relationship between the completion of a
freshman orientation course and retention, whereas other researchers, such as Johnson (1996) and
Pesron and Christensen (1996), studied retention for specific groups of students. In addition, Moore and
Miller (1996) examined how the use of multimedia affects students retention and learning. Furthermore,
the literature also includes descriptions of strategies that specific colleges and universities have found
helpful in retaining students (Bedford and Durkee 1989; Hyman 1995). However, it is also evident that the
findings reported in the literature are often conflicting (Cabrera et al. 1992). For instance, Glass and
Garrett (1995) reported that among community college students retention and grade point average (GPA)
are not related to age, gender, race, employment status, college major, or college attended. On the other
hand, Murtaugh et al. (1999) found that attrition increases with age and decreases with increasing high
school GPA and first-quarter GPA. Statistically significant associations of retention with ethnicity/race,
orientation courses, residency, and college at first enrollment were also noted. Considering these reports
and the often conflicting findings, one can conclude that student attrition is a continuing problem and not
much has been done to effectively improve retention during the last centuries (Glass Jr and Garrett 1995).
Additionally, the majority of these studies did not examine any individual course characteristics but
focused on identifying the most promising students to recruit; a practice that is very different, in principle,
from the massive and open nature of MOOCs.
As far as the methodology of this study is considered, an appropriate method for exploration of the given
phenomenon and theory building relevant to the IS discipline is the Grounded Theory Method (GTM)
(Eisenhardt 1989; Glaser and Strauss 1967). In this regard, GTM provides a flexible way of conducting
research that prioritizes exploration of the given phenomenon in a predominantly inductive theory
development paradigm (Birks et al. 2013). GTM enables researchers to provide a rich description of the
given phenomenon based on a systematic approach through observations, archival data, interviews, and
other sources (Corbin and Strauss 2007). The greatest advantage of GTM is the logic of discovery, rather
than that of verification in data analysis, which is essential to the delicate question of theory building in
grounded research (Vaast and Walsham 2011). The emergent principle of grounded theory is manifested
in the belief that both the outcome (grounded theory) and the research design should be emergent
(Corbin and Strauss 2007). Despite the misconceptions about GTM (Suddaby 2006; Urquhart and
Fernandez 2013), its roots in quantitative methods make GTM a powerful tool also for quantitative
research (Birks et al. 2013) allowing to conduct pioneering research moving between induction and
4. Breakthrough Ideas in IS
4 Thirty Fourth International Conference on Information Systems, Milan 2013
deduction (Corbin and Strauss 2007) with both flexibility and rigor (Birks et al. 2013). In the new era of
wide access to data, researchers can effectively integrate GTM into their work while working within the
research paradigm with which they are most comfortable (Birks et al. 2013).
Moreover, this study also draws on a new stream of research that applies text-mining techniques to
reviews and provides significant opportunities to explore important sources of information. The most
common techniques search for statistical patterns and trends in the text in order to distinguish between
positive and negative reviews (Dave et al. 2003) or use classifiers that determine whether a particular
feature or concept is implicitly discussed (Ghani et al. 2006). For example, Hu and Liu (2004a; 2004b)
summarized all the customer reviews of a product employing association rule mining to find frequent n-
grams to be used as candidate features and Lee and Bradlow (2007) presented an automatic procedure for
obtaining conjoint attributes and levels that list the explicit pros and cons of a product. In a close stream
of research using the sentiment of user-generated content, Pang et al. (2002) studied the rating-inference
problem by classifying movies and, then, Das and Chen (2007) examined bulletin boards and showed that
the aggregate sentiment predicts the stock index. Moreover, Archak et al. (2007; 2011) demonstrated how
textual data can be used for both predicting future changes in sales and learning consumers’ relative
preferences for different product features. Finally, Pang and Lee (2008) offered a comprehensive survey
of the research in the field of sentiment analysis. In general, the employed techniques are similar to those
used in conjoint analysis (Green and Srinivasan 1990) and preference measurement (Netzer et al. 2008),
which also aim at determining how people value different features in a product or service, but they differ
in the source of data used for the analysis.
Recognizing the significance of user-generated content and online reviews, various important aspects
have been studied in relevant streams of literature. Research on computer mediated communication
suggests that online community members communicate information about product or service evaluations
with intent to influence others’ purchase decision as well as provide social information about contributing
members themselves (Postmes et al. 2001). Search time (Goldsmith and Horowitz 2006) and risk
(Hennig-Thurau et al. 2004) reduction have been also identified as motives for using consumer opinion
platforms. Also, recent studies show that personal opinions found on internet forums are more likely to be
perceived trustworthy, less risky, and more relevant to the consumer than other sources (e.g. official
webpages) because users perceive the source to be similar to themselves (Bickart and Schindler 2001).
Finally, the hypothesis that user-generated reviews affect user choices has also received strong support in
empirical studies (Dhar and Chang 2009; Ghose et al. 2012; Liu 2001; Muchnik et al. 2013).
Data Analysis and Coding of Variables
In this section, we explain the methodological ideas and the research model we employed. Also, we
describe the data we collected from various MOOC platforms and CourseTalk.org (2012), a website for
online course reviews, and discuss each variable used in the subsequent analysis. In particular, we
collected qualitative and quantitative data about 133 courses offered by 30 universities and 6 providers
(platforms). The courses were offered by the following providers: Canvas Network, Codecademy, Coursera,
edX, Udacity, and Venture Lab, and belong to a wide range of academic disciplines: Business, Computer
Science, Engineering, Humanities, Mathematics, and Science.1 More specifically, in order to study the
retention of students in MOOCs, we collected and analyzed 1163 textual reviews submitted online by 842
students that participated in at least one course. In addition, following the GTM guidelines, after actively
participating in various MOOCs, observing students, and directly interacting with participants, additional
concepts emerged related to student, course, university, and platform characteristics. These concepts that
affect student retention in MOOCs were identified through several iterations and the corresponding data
was collected. Hence, for each course we manually gathered additional data about the university which
offers the course, the platform where the course is hosted, the academic discipline(s) of the course, the
average difficulty of the course in the posted reviews, whether the course is self-paced or has a calendar-
based schedule, the estimated workload in hours/week, the duration of the course in weeks, whether there
is automated feedback or peer assessment, if team projects or final exams are required, whether a (paid)
1 The Science discipline includes courses in the following sub-disciplines: Medicine, Healthcare, Physical Sciences,
Biology, and Food & Nutrition. Also, a course may belong to more than one academic discipline. Besides, we do not
consider any courses non-affiliated with a formal educational institution, such as those offered by Code School LLC
and SkillShare Inc.
5. Adamopoulos / What Makes a Great MOOC?
Thirty Fourth International Conference on Information Systems, Milan 2013 5
textbook is suggested, and whether students who successfully complete the course receive a certificate.
Besides, for each university we estimate its relative ranking for the academic discipline of each course
offered. In particular, the universities are ranked based on the QS World University Rankings (2012).2
Finally, for a large number of students, we also collected data about their sex and whether they attend a
formal educational institution. Studying the course retention problem, the dependent variable of interest
in our analysis is the self-reported progress of each student in each course, and can be naturally ordered
as following: Course Not Completed (i.e. dropped), Course Partially Completed (e.g. complete 70% of the
course or complete the course without submitting the assignments), and Course Successfully Completed.3
In the next session, we describe in detail the textual analysis we conducted and the variables that we
estimated in the explanatory numerical analysis.
Textual Analysis and Integration of Methods
Using text and opinion mining methods to identify a) the important features that affect student
satisfaction and retention of MOOCs and b) the corresponding evaluation of each student about these
features, we proceed according to the following steps:
We mine MOOC features on which students have commented,
identify opinion sentences in each review and the corresponding opinion words, and
decide whether each opinion sentence is positive or negative and estimate the corresponding sentiment
score for each feature.
To perform these tasks, we made use of both data mining and natural language processing techniques. In
order to identify the concepts that characterize a MOOC and concern students, we followed an approach
similar to the one proposed by Hu and Liu (2004a; 2004b). First, we found those features on which many
people have expressed their opinions. The course features we are interested in are nouns or noun phrases
in review sentences and, hence, we used part-of-speech tagging (Manning and Schütze 1999). Some pre-
processing of words, which includes removal of stop-words and non-English words, stemming, and fuzzy
matching, was also performed. Then, a transaction file was created where each transaction corresponds to
the identified nouns and noun phrases included in a sentence. This file was subsequently used in
association mining (Margahny and Mitwaly 2005) to find all frequent item-sets (a set of words or phrases
that frequently occurs together in some sentences). Those nouns and noun phrases are the features we are
interested in. In particular, in order to identify the important features, we used the Apriori algorithm
(Margahny and Mitwaly 2005) with 1% minimum support. Further, in order to remove unlikely features,
we used both compactness pruning and redundancy pruning (Hu and Liu 2004b).
Then, we implemented two alternative approaches in order to identify the expressed opinion about each
feature discussed in the posted reviews. The first approach, an orientation analysis mechanism, consists
of the extraction of opinion words and the identification of their orientation. Opinion words were
extracted using the corresponding frequent features and the semantic orientation of each opinion word
was identified with the help of WordNet (Fellbaum 1998). In detail, we identified and extracted the
adjectives (opinion words) from those sentences that contained one or more identified features (opinion
sentences); we limited the opinion word extraction only to opinion sentences, as we are only interested in
users' opinions on these features. Then, to predict both the orientation of the adjectives in the collected
reviews and the orientations of the opinion sentences, we selected a set of common adjectives as a seed list
and proceeded as in (Hu and Liu 2004a).4 The alternative approach consists of identifying the sentiment
for each feature in each opinion. To perform this task we used a sentiment analysis mechanism
(AlchemyApi 2012). The two alternative mechanisms for opinion mining produce, in principal, similar
2 If a university is not included in the QS World University Rankings, then it is relatively ranked as next to the
university with the lowest known ranking.
3 When submitting a review, the students could only select one of the predefined options regarding their progress (i.e.
Taking Course Now, Course Not Completed, Course Partially Completed, and Course Successfully Completed). In the
conducted analysis, we did not include any reviews from students currently attending the corresponding course.
4 Opinion word extraction is related to previous work on subjectivity (Bruce and Wiebe 1999) which has established a
positive statistical significant correlation between the expression of an opinion and the presence of adjectives (Hu and
Liu 2004b). Words that encode a desirable state (e.g. beautiful, awesome) have a positive orientation, while words
that represent undesirable states have a negative orientation (e.g. disappointing).
6. Breakthrough Ideas in IS
6 Thirty Fourth International Conference on Information Systems, Milan 2013
results. Because of space restrictions, we focused on the sentiment analysis mechanism and we mainly
used the orientation analysis mechanism to check the robustness of our study.
Additionally, in order to evaluate and verify the results of the aforementioned procedure and also enhance
our data with rich interpretative accounts, we appointed two graduate students and in collaboration we
manually investigated in total 20% of the textual reviews. Following the GTM guidelines and without
having any predefined framework in mind, we conducted a systematic manual exploration of the online
reviews from different standpoints and tried to make sense of the user-generated content. Employing an
iterative approach, through constant comparison of the course reviews, the already identified features of
MOOCs, and the relative relationships of these features, we coded the data identifying the most important
factors that affect the course retention decision. The codes were documented using memos taking the
form of text narratives and diagrams. At each iteration of this process, we were sampling more reviews
identifying potentially new emerging factors that affect a student’s decision to drop a course or not,
abstracting out the concepts from the codes, and contrasting any new features with the existing ones. In
parallel with this passive process of analyzing online user-generated data and in an effort to embrace
multiple sources of data, we also employed a more active engagement as researchers. In particular, we
enrolled as students and participated in several MOOCs by submitting assignments, engaging in
discussions on the bulletin board, and reviewing online material of the courses. Proceeding as previously
described, new factors and interactions were identified and integrated with the already identified concepts
through constant comparison. This active engagement yielded a richer description and different
perspectives of the phenomenon. At the end of each iteration of this process, we conducted a
corresponding econometric analysis and built predictive models to assess the explanatory and predictive
power of the concepts that had already been identified and evaluate the potential need for further analysis
and coding of variables. This process of sampling, iterative manual coding, and constant comparison was
repeated until saturation had been reached and no new concepts came up through examining additional
reviews or making new observations. During the final step of this process, the emergent concepts were
grouped into different categories according to their relationships and differences. Based on this approach,
we came up with a joint list that contained the factors our algorithm had revealed; the employed
categorization was somehow different, grouping tightly themed topics into one single category (e.g. videos
and lectures under course material). Hence, we updated our algorithm to group these specific features
together. Additionally, apart from matching the automated features, new features emerged through the
manual iterative coding process.
The MOOC features that were identified based on the textual analysis and for which the corresponding
opinion of each student (i.e. student course evaluation) was quantified using the sentiment analysis
mechanism, were grouped into the following concepts (variables): Assignments, Course Material,
Discussion Forum, and Professor. Moreover, using the aforementioned research methods, additional
concepts emerged that are related to student (i.e. gender, matriculated student), course (i.e. assignments,
professor, discussion forum, course material, self-paced, difficulty, workload, weeks, certificate, peer
assessment, team projects, suggested textbook, paid textbook, final exam, academic discipline), university
(i.e. ranking, courses offered), and platform (i.e. Canvas Network, Udacity, Venture Lab, Coursera, edX,
Codecademy) characteristics; these variables were manually collected. Table 1 summarizes all the
variables, which were used in the analysis, and shows the corresponding descriptive statistics computed
over all the observations in our data set. Interestingly, Table 1 shows that the average sentiment of the
students for the course evaluation variables (i.e. assignments, professor, discussion forum, course
material) is positive but of small magnitude. In other words, this initial observation confirms that, despite
the broad acceptance of MOOCs among the students and the positive sentiment, there are still
opportunities for further enhancements.
Table 1. Variables and Descriptive Statistics
Variable Description Mean Std.Dev.MinMax
Course Progress
Whether the student did not complete, partially completed,
or successfully completed the course
1.6708 0.5648 0 2
Assignments
The sentiment of the individual review for course
assignments
0.1094 0.1367 -1 1
Professor The sentiment of the individual review for the professor(s) 0.1158 0.1410 -1 1
7. Adamopoulos / What Makes a Great MOOC?
Thirty Fourth International Conference on Information Systems, Milan 2013 7
Discussion Forum
The sentiment of the individual review for the discussion
forum
0.1105 0.1396 -1 1
Course Material
The sentiment of the individual review for the course
material
0.1154 0.1346 -1 1
Self-paced Whether the course is self-paced (dummy) 0.0879 0.2821 0 1
Difficulty The average difficulty of the course 2.8838 0.5387 1 5
Workload The estimated workload in hours/week 6.0714 2.6795 0 17.5
Weeks Duration of the course in weeks 7.8968 3.3293 0 15
Certificate
Whether students who successfully complete the course
receive a certificate (dummy)
0.7136 0.4522 0 1
Peer Assessments Whether there is peer assessment in the course (dummy) 0.2218 0.4157 0 1
Team Projects Whether there is any team project in the course (dummy) 0.0091 0.9474 0 1
Suggested Textbook Whether there is a suggested textbook (dummy) 0.3353 0.4723 0 1
Suggested Paid TextbookWhether there is a suggested paid textbook (dummy) 0.1625 0.3691 0 1
Final Exam/Project
Whether there is a final exam or project in the course
(dummy)
0.3715 0.4834 0 1
Interested Users
The number of user on the review platform interested in
attending the course
24.7933 18.1152 0 74
Course Reviews The number of reviews posted for the course 45.2630 41.2266 1 126
University Ranking Relative ranking of the university 12.1786 7.0543 1 30
Courses Offered The number of courses offered by the university 16.4372 9.4899 1 34
Business & Management
Whether the course belongs to the Business & Management
discipline (dummy)
0.1470 0.3543 0 1
Computer Science
Whether the course belongs to the Computer Science
discipline (dummy)
0.3972 0.4895 0 1
Engineering
Whether the course belongs to the Engineering discipline
(dummy)
0.0353 0.1845 0 1
Humanities
Whether the course belongs to the Humanities discipline
(dummy)
0.3525 0.4780 0 1
Mathematics
Whether the course belongs to the Mathematics discipline
(dummy)
0.0404 0.1970 0 1
Science
Whether the course belongs to the Science discipline
(dummy)
0.1273 0.3334 0 1
Canvas Network
Whether the course is offered on the Canvas Network
platform (dummy)
0.0009 0.0293 0 1
Udacity
Whether the course is offered on the Udacity platform
(dummy)
0.0598 0.2315 0 1
Venture Lab
Whether the course is offered on the Venture Lab platform
(dummy)
0.0069 0.0827 0 1
Coursera
Whether the course is offered on the Coursera platform
(dummy)
0.8783 0.3305 0 1
edX Whether the course is offered on the edX platform (dummy) 0.0559 0.2298 0 1
Codecademy
Whether the course is offered on the Codecademy platform
(dummy)
0.0026 0.0507 0 1
Student Gender Whether the student is male (dummy) 0.5994 0.4903 0 1
Matriculated Student
Whether the student attends a formal educational
institution (dummy)
0.2415 0.4285 0 1
Figure 1 summarizes the proposed research model illustrating the categories that emerged in our analysis.
In particular, following the GTM guidelines and triangulating different data sources, we suggest that the
8. Breakthrough Ideas in IS
8 Thirty Fourth International Conference on Information Systems, Milan 2013
decision of a student to drop a course is affected by the student course evaluation (e.g. evaluation of the
professor from individual student), the course characteristics (e.g. difficulty, academic discipline),
university characteristics (e.g. university ranking), platform characteristics (e.g. platform usability), and
student characteristics (e.g. gender).
Figure 1. A Model of Online Course Retention.
Explanatory Econometric Analysis
In this section, we present the results of our explanatory econometric analysis, which examines the
importance of each factor in student retention in MOOCs (in the next session, we will describe our
predictive model, based on machine learning techniques). Through our analysis, we employ ordered
choice models in a hedonic-like framework, where we use text and opinion mining to model qualitative
opinions in quantitative evaluation scores, and we aim to provide a better understanding of how students’
decisions are affected by the different factors.
In prior literature on traditional education, various methods have been employed to study student
retention including simple cross-tabulation (Avakian et al. 1982), two-sample comparisons (Naretto 1995),
linear regression, logistic regression, probit analysis (Dey and Astin 1993), and Markov processes
(Heiberger 1993). In some prior works, the research methods applied to the problem of student retention
have been criticized for issues related to their consistency, efficacy, and suitability (Dey 1997; Murtaugh et
al. 1999; Nandeshwar et al. 2011); this might be the explanation for the conflicting findings reported in
literature. For instance, the use of linear regression in our study, because of the unboundness problem
and the assumption of a continuous dependent variable instead of a discrete non-numeric outcome, could
lead to erroneous findings (Greene 2003). In our analysis, we take advantage of the nature of our data and
we model course retention as a countable, discrete, ordered choice by giving meaningful numeric values to
student choices and estimate the corresponding probabilities; each student chooses among not to
complete a course, to partially complete it (e.g. complete 70% of the course or complete the course
without submitting the assignments), or to successfully complete it. Our goal is to understand the
behavioral process that leads to the agent’s choice and, thus, we take a causal perspective since there are
factors that collectively determine, or cause, the agent’s choice (Train 2003). Since there are also factors
not observed by the researcher, such as unobservable abilities of students, the agent’s choice is not
deterministic and cannot be predicted exactly. Instead, the probability of any particular outcome is
derived in a consistent, invariant, and efficient (given the model) way (Greene 2003). Based on the above
discussion and since an underlying rational decision process takes place when a student decides to
complete a course or not, we use ordered choice models to efficiently model student retention. The model
platform is an underlying random utility model or latent regression model (McFadden 1974),
in which the continuous latent utility or measure, , is observed in discrete form through a censoring
9. Adamopoulos / What Makes a Great MOOC?
Thirty Fourth International Conference on Information Systems, Milan 2013 9
mechanism:
The vector is the set of the covariates of our model and is the vector of parameters to be estimated. In
order to identify the factors that affect the decision of the users and examine their relative importance, we
fit different specifications for the course retention choice model that correspond to iterations described in
the previous section. In particular, Model 1 studies the effect of specific student course evaluation (i.e.
sentiment of the student with respect to assignments, professor, discussion forum, and course material),
course (i.e. difficulty, workload, weeks, certificate), and university (i.e. university ranking) characteristics.
Then, Model 2 uses the additional information of academic discipline (course characteristic) and Model 3
controls also for the characteristics and idiosyncrasies of the various platforms (platform characteristics).
Finally, Model 4 uses the interactions between various course characteristics, controls for the popularity
of the course, and the prominence of the university on MOOCs, Model 5 uses also the gender of the
student, and Model 6 controls for students attending a formal educational institution; a smaller number
of observations was available for Model 5 and Model 6. Fitting different specifications also allows us to
test the robustness of our models. The results obtained using the two alternative methods for opinion
identification, described in the previous section, are similar and, henceforth, only results using the
sentiment analysis mechanism are presented, because of the space limitations. Table 2 presents the
results obtained using the different specifications for the logit ordered choice model; similar results were
also obtained using the probit order choice model specification.
Table 2. Course Retention Ordered Choice Model
Estimates Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Constant
3.9598*** 4.9278*** 6.0277*** 13.4146*** 15.3925*** 14.6814***
(0.6170) (0.8286) (1.6078) (2.9766) (3.1672) (4.2600)
Assignments
1.5263 2.2288* 2.1729* 2.2127* 2.1999 1.0635
(1.2364) (1.2806) (1.2832) (1.3021) (1.3903) (2.3073)
Professor
2.3389* 2.1863* 2.1834* 2.2163* 2.1886* 2.7785
(1.2383) (1.2560) (1.2542) (1.2580) (1.3067) (2.0393)
Discussion Forum
-3.8139** -3.7593** -3.6273** -3.7215** -4.0392** -5.0402*
(1.5970) (1.6440) (1.6461) (1.6489) (1.7538) (2.7933)
Course Material
0.6782 0.5295 0.5350 0.5793 0.7241 2.6901
(1.2708) (1.2726) (1.2770) (1.2585) (1.3247) (2.2039)
Self-paced
-0.4641 -0.4639 -0.3851 -10.716*** -16.8596* -17.9977*
(0.5377) (0.5564) (0.6850) (3.3558) (8.8453) (10.8333)
Difficulty
0.3041* -0.0887 -0.0958 -2.7487*** -2.9125*** -2.2418*
(0.1584) (0.1853) (0.1890) (0.7493) (0.7920) (1.2399)
Workload
-0.1664*** -0.0924* -0.0764 -0.2103 -0.4135 -0.7555
(0.0437) (0.0471) (0.0493) (0.2650) (0.2914) (0.4843)
Weeks
-0.1296*** -0.1203*** -0.1252*** -0.6288*** -0.6171** -0.3342
(0.0340) (0.0379) (0.0391) (0.2364) (0.2453) (0.3713)
Certificate
0.3799** 0.0116 -0.0937 0.2114 0.0026 -0.3241
(0.1677) (0.1918) (0.2086) (0.2491) (0.2714) (0.4108)
Peer Assessments
1.7670*** 1.3055*** 1.3606*** 0.9291*** 1.1171*** 0.5559
(0.2706) (0.2915) (0.3011) (0.3438) (0.3610) (0.5152)
Team Projects
-1.9212** -1.4399 -1.5771* -1.2295 -1.9028** -4.8089***
(0.8723) (0.8762) (0.8896) (0.9216) (0.9461) (1.7193)
Suggested Textbook
0.0691 1.0653*** 1.1038*** 1.3616*** 1.4586*** 1.7157***
(0.2855) (0.3455) (0.3476) (0.3991) (0.4279) (0.6053)
Suggested Paid Textbook
0.0380 -1.2164*** -1.1827*** -1.3849*** -1.6432*** -1.9530***
(0.3475) (0.4275) (0.4348) (0.4652) (0.4948) (0.7013)
Final Exam/Project
1.2159*** 0.8153*** 0.7559*** 0.4593* 0.3408 0.1223
(0.1985) (0.2154) (0.2301) (0.2594) (0.2717) (0.3748)
University Ranking
-0.0321*** -0.0270** -0.0390** -0.0415* -0.0653*** -0.0567
(0.0119) (0.0124) (0.0179) (0.0229) (0.0245) (0.0345)
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10 Thirty Fourth International Conference on Information Systems, Milan 2013
Business & Management
0.8299** 0.8536** 1.0338*** 1.1646*** 0.8711
(0.3468) (0.3483) (0.3737) (0.3987) (0.5535)
Computer Science
0.5235* 0.4959 0.8257** 0.4778 -0.5413
(0.3087) (0.3154) (0.3982) (0.4321) (0.6218)
Engineering
0.0010 -0.0081 0.1848 0.0111 -0.4097
(0.5490) (0.5529) (0.6026) (0.6462) (1.0279)
Humanities
-0.8842** -0.8464** -0.7931** -0.9107** -1.1150*
(0.3593) (0.3654) (0.4035) (0.4326) (0.6303)
Mathematics
-0.6146 -0.6582 -0.5737 -0.6943 -0.5447
(0.4121) (0.4187) (0.4426) (0.4628) (0.6091)
Science
0.3471 0.4381 0.6384 0.2107 -0.2312
(0.3718) (0.3805) (0.4318) (0.4494) (0.6042)
Self-paced×Difficulty
4.0867*** 5.1084 4.8426
(1.1802) (3.3601) (4.1269)
Difficulty×Workload
0.1042* 0.1667** 0.2401*
(0.0624) (0.0708) (0.1227)
Difficulty×Weeks
0.2367*** 0.2000** 0.0425
(0.0869) (0.0882) (0.1306)
Workload×Weeks
-0.0248 -0.0200 -0.0013
(0.0209) (0.0222) (0.0373)
Interested Users
0.0074 0.0109 0.0191
(0.0078) (0.0082) (0.0117)
Course Reviews
0.0052 0.0048 -0.0058
(0.0037) (0.0039) (0.0059)
Courses Offered
-0.0153 -0.0181 -0.0031
(0.0134) (0.0138) (0.0197)
Student Gender
0.1702 0.2342
(0.1853) (0.2879)
Matriculated Student
-0.4539
(0.2870)
Platform dummies - - Yes Yes Yes Yes
N 1163 1163 1163 1163 1043 441
Restricted log likelihood -834.490 -834.490 -834.490 -834.490 -775.724 -310.695
K 17 23 28 35 36 37
Log likelihood function -701.310 -676.181 -674.883 -664.827 -609.834 -260.460
Chi squared 266.3575 316.6166 319.2124 339.325 331.780 100.470
Significance level 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
McFadden Pseudo 0.1595 0.1897 0.1912 0.2033 0.2139 0.1617
Inf.Cr.AIC 1436.6 1398.4 1405.8 1399.7 1291.7 590.9
AIC/N 1.235 1.202 1.209 1.203 1.238 1.340
Note: ***, **, * Significance at 1%, 5%, 10% level.
As Table 2 illustrates, all the examined specifications are statistically significant (we reject the hypothesis
that all the slope coefficients are zero with p<0.001) and do not violate the proportional parallel lines
assumption (Agresti 2002). Also, the academic disciplines and the platform dummy variables provide
significant explanatory power to our model. As we can see, the extended logit model (Model 5), with
course characteristics, academic disciplines, platform indicators, and the gender of the student, provides
the best fit to our data with McFadden pseudo = 0.2139, which represents a very good fit for an
ordered choice model (McFadden 1978).
Interpretation of the coefficients in the ordered choice models is more complicated than the ordinary
regression setting. There is no natural conditional mean function in the model to analyze (this is a
characteristic of discrete choice models), the outcome variable is merely a label for the non-quantitative
outcomes, and the coefficients of the logit model are usually larger than the probit because of the
functional form (Greene 2003). In order to attach meaning to the parameters, the partial effects should be
examined. Table 3 presents the partial effects on the probability of a student successfully completing a
course at means using the aforementioned models of logit ordered choices; very similar results were also
obtained using the probit order choice model specification.
11. Adamopoulos / What Makes a Great MOOC?
Thirty Fourth International Conference on Information Systems, Milan 2013 11
Table 3. Partial effects on Prob[Y=Course Successful Completion] at means
Estimates Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Assignments 0.2592 0.3791* 0.3700* 0.3698* 0.3872 0.1711
Professor 0.3973* 0.3719** 0.3718* 0.3704* 0.3852* 0.4471
Discussion Forum -0.6478** -0.6394*** -0.6177** -0.6219** -0.7109** -0.8111*
Course Material 0.1152 0.0901 0.0911 0.0968 0.1275 0.4329
Self-paced -0.0873 -0.0874 -0.0715 -0.8981*** -0.7859*** -0.8293***
Difficulty 0.0516* -0.0151 -0.0163 -0.4593*** -0.5126*** -0.3607*
Workload -0.0282*** -0.0157* -0.0130 -0.0352 -0.0728 -0.1216
Weeks -0.0220*** -0.0205*** -0.0213*** -0.1051*** -0.1086** -0.0538
Certificate 0.0675** -0.0020 -0.0158 0.0363 0.0005 -0.0493
Peer Assessments 0.2253*** 0.1792*** 0.1854*** 0.1330*** 0.1666*** 0.0801
Team Projects -0.4363** -0.3213 -0.3552 -0.2663 -0.4356** -0.7699***
Suggested Textbook 0.0116 0.1638*** 0.1693*** 0.2000*** 0.2275*** 0.2374***
Suggested Paid Textbook 0.0064 -0.2491** -0.2416** -0.2850*** -0.3539*** -0.4048***
Final Exam/Project 0.1889*** 0.1306*** 0.1217*** 0.0742* 0.0588 0.0196
University Ranking -0.0055*** -0.0046** -0.0066** -0.0069* -0.0115*** -0.0091
Business & Management 0.1184*** 0.1214*** 0.1383*** 0.1604*** 0.1161*
Computer Science 0.0863* 0.0820 0.1314** 0.0814 -0.0883
Engineering 0.0002 -0.0014 0.0294 0.0020 -0.0735
Humanities -0.1609** -0.1538** -0.1412* -0.1685** -0.1985
Mathematics -0.1205 -0.1303 -0.1100 -0.1419 -0.0998
Science 0.0547 0.0678 0.0924* 0.0355 -0.0392
Self-paced×Difficulty 0.6829*** 0.8991 0.7793
Difficulty×Workload 0.0174* 0.0293** 0.0386*
Difficulty×Weeks 0.0396*** 0.0352** 0.0069
Workload×Weeks -0.0042 -0.0035 -0.0002
Interested Users 0.0009 0.0019 0.0031
Course Reviews 0.0009 0.0009 -0.0009
Courses Offered -0.0028 -0.0032 -0.0005
Student Gender 0.0302 0.0386
Matriculated Student -0.0781
Platform dummies - - Yes Yes Yes Yes
Note: Marginal Effects for dummy variables are .
Note: ***, **, * Significance at 1%, 5%, 10% level.
As Table 3 shows, using Model 5, which provides the best fit to our data, Professors have the largest
positive significant effect (0.39, p<0.1) on the probability of a student to successfully complete a course.
Assignments (0.39) and Course Material (0.13) also have positive effects on the successful completeness
of a course whereas the Discussion Forum has a negative effect (-0.71, p<0.1). In other words, the more
satisfied (i.e. positive sentiment) a student is with the professor, the teaching material, and the
assignments, the more probable that s/he will successfully complete the course. However, a negative
effect for the satisfaction of the user with the discussion forum was estimated. Examining the partial
effects of Discussion Forum on the other possible outcomes, the sentiment of the student about the
discussion forum has a small positive effect (0.09) on the probability of the student not to complete the
course but a stronger positive effect on the probability of the student to partially complete the course
(0.62, p<0.05). One possible explanation which we investigated is that mostly students who are not
satisfied or have difficulties with the course and are more likely not to successfully complete a course are
using the online discussion forum and then comment on this in their reviews, but no statistically
significant difference was observed on the distributions. Another possible explanation would be that
students who are more likely not to complete a course have low expectations for the discussion forums
and, hence, they express more easily a positive sentiment, but no statistically significant difference in the
mean values among the different groups was found. Furthermore, Table 3 also shows that self-paced
courses compared to courses that follow a specific timetable have a negative effect (-0.79, p<0.01). In
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12 Thirty Fourth International Conference on Information Systems, Milan 2013
addition, the difficulty of the course (-0.51, p<0.01), the workload (hours/week) (-0.07), and its duration
in weeks (-0.11, p<0.05) have a negative effect on student retention. Besides, peer assessment has a
positive effect (0.17, p<0.01) compared to automated feedback indicating that better technological
solutions for automatically providing feedback and evaluating the assignments of the students are still
needed. Although, final exams and projects make the courses more engaging and have a positive effect
(0.06), team projects that require the active collaboration with other students do not have the same effect
(-0.44, p<0.05). Interestingly, there is a positive effect (0.23, p<0.01) when there is a suggested textbook
on the syllabus that the students can refer to; however, there is a negative effect (-0.35, p<0.01) if this is a
paid textbook to which most of the students do not have access. Moreover, whether a certificate is
awarded upon the successful completion of a course has also a small but positive effect (0.0005). Further,
the better a university is considered and the higher it is ranked, the more likely that a student will
successfully complete a course (-0.01, p<0.01). In addition, the popularity of the course (i.e. number of
users interested in the course on the review platform and number of posted course reviews) and the
prominence of the university (i.e. number of courses offered by the specific university) do not have a
significant effect. Also, based on the interaction terms, for more difficult courses, self-paced courses
(0.90), longer duration in weeks (0.04, p<0.05) and more workload (0.03, p<0.05) have also positive
effects on the probability of a student to successfully complete a course. Besides, Table 3 illustrates that
courses which belong to the disciplines of Business and Management, Computer Science, and Science
have also a positive significant effect in contrast to other disciplines. Finally, there were not found any
statistically significant effects based on the gender of students, whether a student attends a formal
educational institution, or the various MOOC platforms.
Predictive Modeling
The explanatory study that we described above revealed what factors influence course retention and their
relative importance. In this section, we switch from explanatory modeling to predictive modeling. In
other words, the main goal now is not to explain which factors affect the course retention, but to examine
how well we can predict whether a student will successfully complete a course. This method will also
provide us evidence of whether there are other concepts discussed in the user-generated reviews that we
have not identified in the proposed approach. For this multiclass classification problem, we train a
Random Forests classifier (Breiman 2001) employing a holdout validation scheme (Provost and Fawcett
2013) with 80/20 split of data, and we evaluate each model based on the F1-score measure.5
Table 4. Classification Performance of the Predictive Model
Student
Course
Evaluation
Course
Characteristics
University
Characteristics
Academic
Disciplines
Platform
Characteristics
Student
Characteristics
Review
Nouns
Sentiment
Review
Corpus
Random
Forest
Classifier
- - - - - - - - 0.5887*
- - - - - - - 0.6170
- - - - - - 0.7728
- - - - - 0.7973
- - - - 0.8004
- - - 0.8128
- - 0.8108
- 0.7825
- 0.8084
Note: * The Baseline corresponds to predicting the most frequent class in the training set (i.e. “Course Successfully
Completed”).
Table 4 presents the results of our experimental evaluation. The first line shows the Baseline method and
corresponds to predictions made using the most popular class (i.e. “Course Successfully Completed”) in
our train set. Each of the next lines shows the experimental performance of our predictive model using the
5 Other models that were tested and yielded similar results include decision trees (Breiman et al. 1984), one vs. one
multiclass strategy with support vector classification (Chang and Lin 2011), and one vs. all multiclass strategy with
logistic regression (Yu et al. 2011) and ridge regression.
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Thirty Fourth International Conference on Information Systems, Milan 2013 13
corresponding features (i.e. columns of Table 4). The first result that we observe is that using a classifier
with the features that emerged based on our textual analysis (i.e. Assignments, Professor, Discussion
Forum, and Course Material), labeled here as Course Evaluation Features, outperforms the standard
Baseline method by 4.8% in terms of the F1-score (i.e. 0.62 F1-score). Besides, Table 4 illustrates that the
course characteristics features (i.e. self-paced, difficulty, workload, etc.) provide significant predictive
power to our model (i.e. 0.77 F1-score). Moreover, there is further increase in performance using the
university ranking (university characteristics), the academic disciplines (course characteristics), and the
platform dummies (platform characteristics). However, there is no further improvement using student
characteristics. The best performance (i.e. 0.81 F1-score) outperforms the standard Baseline by 38.1%
and was achieved using all the aforementioned features.
Finally, in order to test whether there are any features that affect student retention and have not yet been
identified in our textual analysis, and hence to examine whether we could further improve the
performance of our models, we also use the estimated sentiment for each noun and noun group included
in the review (labeled as Review Nouns Sentiment) as well as the pre-processed corpus of the reviews.
Table 4 shows that the performance of the predictive model was reduced and that these extra features
introduced more noise than predictive power. This means that we have successfully identified all the
features that affect student retention and are discussed in user-generated online course reviews.
Discussion
With the rapid expansion of Massive Open Online Courses (MOOCs), special online communities have
emerged in order to reduce the search costs associated with the proliferation of platforms and courses. In
such online communities, the user-generated course reviews are an important and unique source of
information for both students and researchers who want to better understand the phenomenon of MOOCs.
Working toward the direction of improving MOOCs and reducing attrition, in order to conduct a more in-
depth analysis of the information captured by user-generated content, we integrate multiple research
methods. In this paper,
We explore one of the most important problems of MOOCs and open education identifying both the
concepts that affect online course retention and their relative impact.
We illustrate the use of the Grounded Theory Method in a quantitative research, a less frequently
applied paradigm, and integrate state-of-the-art econometric, text mining, opinion mining, and
machine learning techniques, building both explanatory and predictive models.
We contribute to the related literature discovering new rich findings and provide actionable insights to
solve a problem of scientific and societal interest about a breakthrough phenomenon.
In particular, seeking to provide a deep understanding of the phenomenon and develop theory grounded
in empirical observations, the Grounded Theory Method (GTM) is the most appropriate research method
for this study. Despite the misconceptions about GTM (Suddaby 2006; Urquhart and Fernandez 2013), its
roots in quantitative methods make GTM a powerful tool also for quantitative research by prioritizing the
exploration of the given phenomenon and allowing to conduct research with both flexibility and rigor
(Birks et al. 2013; Corbin and Strauss 2007). The greatest advantage of GTM is the logic of discovery,
rather than that of verification, which is essential to the delicate question of grounded theory building
(Vaast and Walsham 2011). Another advantage of GTM as inductive methodology for theory generation is
that it allows the discovery of causal relationships and not just correlations. Besides, many IS researchers
have already relied upon GTM to develop new theory, while its use could be further promoted through the
integration with explanatory and predictive techniques. This integration of GTM with econometric and
machine learning techniques could bring radically different approaches and new theories to the IS field
due to the innovative possibilities of “big data.” Moreover, such methods can lead to scalable and
reproducible research and more consistent results alleviating also researcher biases. This integration also
allows researchers to consistently quantify qualitative data in a continuous numerical scale (e.g. opinion
mining and sentiment analysis) and evaluate the predictive and explanatory power of the emergent theory.
In this study, as GTM requires, we avoided using specific theories as the starting point or formulating the
hypotheses in advance (preconceived hypotheses result in a theory that is ungrounded from the data).
Nonetheless, hypothesis generation in GTM is essentially the statement of probabilities that explain latent
patterns of social behavior (Glaser 1998) and, thus, fairly fits with the goals of choice models. Overall, we
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14 Thirty Fourth International Conference on Information Systems, Milan 2013
let the data speak, not by imposing beforehand a conceptual framework upon it, but through an inductive
approach, which avoids pre-existing knowledge bias, and some back and forth between a conceptual and
an empirical plane. In the same way, we let also the theory emerge from the data and become gradually
refined through comparison of existing theories.
Furthermore, we decided to use a real-world data set with user-generated information in order to conduct
our analysis. In contrast to data collected from surveys or course evaluations, which usually involves a low
number of responses about only few courses and only touches on some aspects of a course while aspects
not obvious to faculty are ignored, this user-generated information provides a holistic view and can be
effectively used to improve students’ experience with MOOCs by understanding some of their most
important problems. Also, the specifics of the constructed data set allow for a more granular level of
analysis. Besides, another major advantage of our data set is that course reviews are submitted by
students that consciously registered in the corresponding course and intended to successfully complete it.
Hence, students’ reviews can be used to estimate better models of course retention compared to the use of
average statistics (e.g. per cent passed the course based on course start).
Implications for Research and Theoretical Integration
Based on the prior literature on student retention in traditional education and the initial discussion
presented in this paper, the aforementioned results both quantify and verify the conceptualized high
significance of professors and instructors in MOOCs (Cormier and Siemens 2010; Masters 2011). Also, the
finding that the likelihood of dropping a course depends on the academic discipline is partially supported
by prior research on community colleges affirming that while some areas attract students with a strong
ability to adapt to online coursework, others attract students who do not adapt well (Xu and Jaggars 2013).
Similarly, using a different classification on broad fields of education, Olsen (2008) found that Australian
fulltime students in Health, Engineering, Management, Architecture and Education stayed the course
better than students in other disciplines, but only aggregate statistics across all students were used and no
marginal effects. Further, our results are also supported by qualitative studies suggesting that students
prefer to take difficult courses in a traditional setting rather than online (Jaggars 2012) and that
demographics variables do not predict the likelihood of dropping from online courses (Glass Jr and
Garrett 1995; Pantages and Creedon 1978; Willging and Johnson 2004). Also, our findings are in
accordance with prior research on the effect of difficulty (Xenos et al. 2002), workload (Tresman 2002),
and team projects (Knight et al. 2007) on course retention in traditional educational settings. In addition,
our finding about discussion forums may seem to partially contradict that retention in traditional
education (not individual courses) could be improved by increasing the integration of students into the
academic and social systems of the college (Tinto 1987). However, one possible explanation is a selection
effect according to which students who are more likely not to successfully complete a course have low
expectations for the discussion forums and, hence, they express more easily or more often a positive
sentiment, but no statistically significant difference was found. Another possible explanation is that
discussion forums can reduce student retention because of the diversity among the students. In particular,
variations in levels of expertise and in individual online behavior can limit connectivity and lead to the
formation of groups. Group formation in turn can reduce possibilities for autonomy, openness and
diversity, which in turn can reduce the opportunities for connectivity, connectedness and engagement,
and so on (Mackness et al. 2010). Finally, our findings contradict prior results on self-pacing versus
instructor-pacing courses (Morris et al. 1978) while we provide richer explanations accounting for many
interaction effects and controlling for various factors.
Because of the novelty of MOOCs and the different levels of analysis in the prior literature on traditional
education, most of our findings cannot be directly compared to the results of previous studies. However,
the aforementioned findings can also be confirmed using the actual conversations of the students on the
MOOC platforms. For instance, discussing automated feedback various students have commented that
“the autograder was very frustrating at times”, whereas other students mentioned that “the weekly
projects made it very fun and having to grade other students gave me a glimpse into how others are
solving the same problem”. Besides, many reviews discuss also the inefficiencies of discussion forums
explaining that students “dropped this class as it was unbalanced and the team did not respond to issues
in a well-rounded manner.” Also, as far as the final projects are considered, there were reviews
mentioning that “the course project has everyone excited about it as we are addressing real life issues and
solving them in our own way.” In addition, the positive effect of textbooks is also depicted on numerous
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Thirty Fourth International Conference on Information Systems, Milan 2013 15
posts referring to MOOCs that “follow a very good textbook written by the instructor.” Similar comments
are also common for other course components of MOOCs (e.g. “the professor is a great teacher”, “the
assignments were hard but the lecture material was excellent and easy to follow”). Finally, even though a
small fraction of the students expressed different preferences across the various MOOC platforms, they
did not consider these differences to be significant but “more of a personal choice”.
Managerial Implications and Policy
This study also provides several insights with significant implications for course design, the use of
technology in learning environments, and educational policy. Based on the results, in order to increase the
engagement of students and reduce dropout rates, online courses should be of average difficulty for the
students (i.e. less than 2.9 on a scale of 1 to 5) and require a moderate workload (i.e. about 6 hours/week)
without spanning many weeks (i.e. less than 8 weeks). Also, courses should have a calendar-based
schedule and final exams or projects. Moreover, the use of textbooks is beneficial for the students since
they can refer to standard offline sources. However, the use of paid textbooks has the opposite results
since it restricts the access to important information for a large portion of the students. In addition, peer
assessment results in increased engagement compared to automatic grading, which indicates that better
technological solutions are still needed. Interestingly, our results also confirm that the current form of the
certifications provided in MOOCs is of limited usefulness to the students. The certificates should be
enhanced offering both knowledge verification and student identification. Besides, public policy could
also evaluate the credit-equivalency of MOOCs in order to increase retention. Further, our results about
discussion forums indicate that platforms need to better facilitate knowledge construction and
communication among the students. For instance, the introduction of wikis can supplement and enhance
the discussion forums by providing a reference for codified knowledge separated from the miscellaneous
communication. Finally, the finding that traditional university rankings do not significantly affect course
retention in MOOCs indicates that the content of a course is becoming more important. Universities that
offer MOOCs can build their reputation based on the content they offer and, thus, new significant players
might emerge in education. As a consequence, college and university rankings should be updated in order
to take into consideration these new factors when they evaluate educational institutions.
Apart from the implications in education and the methodological ideas of GTM, the presented analytical
approach has also important managerial implications in other domains, such as social media and online
commerce. For instance, the proposed approach can be effectively used to identify important attributes of
various products and dynamically measure and monitor the corresponding sentiment on social media,
estimate their relative effect on sales and brand perception, actively manage dissatisfied customers on
real-time, and accurately predict churns. Such research also raises keys issues concerning the systematic
discovery of new theories and interesting patterns through the analysis of “big data.”
Limitations and Future Research
Nevertheless, we should be careful interpreting the aforementioned findings since MOOCs are only a
recent development and all the providers, students, and platforms are still adjusting to this new
phenomenon. For instance, after this initial adjusting period, students may start focusing also on other
aspects of the courses. This hypothesis is partially supported by the fact that the main characteristics of
the courses that students evaluate in their reviews are not very different from the ones evaluated in a
traditional educational setting. Thus, future research should re-examine the described problem and study
the adoption process among the students. Another limitation of our work is that the course retention
variable was self-reported by the students and not directly acquired from the course providers.
Additionally, in the quantitative analysis we only use data about students that submitted at least one
course review and not all the students that attended any of these courses. Besides, no specific features of
the platforms, such as usability and technical characteristics, were included in the conducted analysis; to
partially address this limitation we use indicator variables for the platform that hosts each course. Also,
the problem of course retention should be studied vis-à-vis the satisfaction of the students and the
corresponding course evaluation.
Moreover, future research is needed to look into how online socialization influences the dropout decision
of students in MOOCs. Prior work on traditional education has illustrated that social life has large
significant effects on institutional fit for each class (Pascarella 1980; Spady 1970; Tinto 1975) and has
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16 Thirty Fourth International Conference on Information Systems, Milan 2013
indicated that students have a much greater effect on the attitudes of other students than do faculty
members, considering them the primary agents of socialization in this type of academic environment
(Bean 1985). Also, future work should look into the interactions between the individual and the academic
and social systems. In particular, researchers should closely study individual student goals (e.g.
importance of successfully completing the course), attributes (e.g. ability), educational background and
academic performance (e.g. academic major, grade-point average, and academic attainments), family
background (e.g. social status attributes), faculty contact and interaction (e.g. type and frequency of
contact), as well as environmental factors (e.g. finances, employment rates).
Conclusions
Massive Open Online Courses (MOOCs) have remarkably expanded during the last years offering a
rapidly growing number of courses across various platforms. However, the very high drop-out rates
indicate that much more should be done in order to satisfy the actual educational needs of the students.
Tackling this important problem, we employ the Grounded Theory Method (GTM) on quantitative data, a
less frequently applied paradigm. In particular, we present an innovative analysis using a real-world data
set with user-generated online reviews, where we both identify the different student, course, platform,
and university characteristics that affect student retention and study their relative effect. An important
aspect of the conducted analysis is the integration of econometric, text mining, opinion mining, and
machine learning techniques, building both explanatory and predictive models, toward a more complete
analysis of the information captured by user-generated content. Extending the methodological ideas of
GTM to explanatory quantitative analysis and going beyond descriptive statistics of coded verbal data, we
both contribute to the related literature discovering new rich findings and provide actionable insights with
implications for both MOOCs and education. The use of radically different approaches and fresh
interdisciplinary perspectives in this paper offers the potential to inspire future research and open up new
streams of scientific inquiry for the IS field.
The findings of our analysis illustrate that Professor(s) is the most important factor in online course
retention and has the largest positive effect on the probability of a student to successfully complete a
course. The sentiment of students for Assignments and Course Material also has positive effects on the
successful completeness of a course whereas the Discussion Forum has a positive effect on the probability
to partially complete a course. Furthermore, self-paced courses have a negative effect, compared to
courses that follow a specific timetable. In addition, the difficulty, the workload, and the duration of a
course have a negative effect. On the other hand, for the more difficult courses, self-paced timetable,
longer duration in weeks, and more workload have a positive effect on the probability to successfully
complete a course. Besides, final exams and projects, open textbooks, and peer assessment have also
positive effects. Moreover, whether a certificate is awarded upon the successful completion of a course
also affects retention. Additionally, the better a university is considered (i.e. higher ranking), the more
likely that a student will successfully complete a course. Further, our results illustrate that the courses
which belong to the academic disciplines of Business and Management, Computer Science, and Science
have a positive significant effect in contrast to courses in other disciplines (i.e. Engineering, Humanities,
and Mathematics). Finally, attrition was not found to be related with student characteristics (i.e. gender,
formal education). The aforementioned findings were also verified by the predictive study we conducted.
The employed predictive modeling techniques also indicate that we have successfully identified all the
features that affect course retention and are discussed in user-generated course reviews.
The proposed approach and the corresponding findings have several implications for the universities and
platforms offering online courses and can be used for real-time detection of dissatisfied students as well as
the design of better and more engaging courses that will increase retention rates. In particular, the results
suggest that the course characteristics (e.g. estimated difficulty, workload, duration, whether there is
automated grading, etc.) are important determinants of students' satisfaction and suggest useful
guidelines for course design. For instance, MOOCs in general should have a specific instructor-based
timetable, but for the most difficult courses students should be allowed to follow their own pace. Also, the
findings suggest that there is room for improvement in the current form of certifications which should be
redesigned in order to become more useful for the students and better motivate them to successfully
complete the corresponding course. Moreover, the positive effect of the sentiment of students for the
discussion forum on the probability to partially complete the course illustrates that better mechanisms or
17. Adamopoulos / What Makes a Great MOOC?
Thirty Fourth International Conference on Information Systems, Milan 2013 17
complementary technologies, such as wikis, are still needed in order to successfully advice, assist, connect,
and motivate the students. In addition, the finding that courses in the disciplines of Business and
Management, Computer Science, and Science are more likely to be successfully completed may suggest
that either specific types of courses are better suited for online education or are better accepted by the
students; an issue that should be addressed by future research. Finally, apart from the implications in
education and the methodological ideas of GTM, we also discuss the managerial implications of the
proposed analytical approach in other domains, such as social media and online commerce.
As part of the future work, we will study the dynamics of student online communities that participate in
discussion forums of MOOCs. Furthermore, we will study how different factors affect the popularity of
MOOCs and the helpfulness (Ghose and Ipeirotis 2011) of online MOOC reviews in order to reduce the
search costs for students and enable them to make more informed decisions. Moreover, we will also study
the student dropout from online education and not just from individual online courses. Finally, the
growing number of available courses for each academic discipline and topic will soon make it even harder
for students to locate the best MOOCs for them, and thus we plan to develop a recommender system
(Adamopoulos and Tuzhilin 2013b; Adomavicius and Tuzhilin 2005) to deliver to students personalized
unexpected (Adamopoulos 2013; Adamopoulos and Tuzhilin 2011; Adamopoulos and Tuzhilin 2013a)
recommendations for courses, which fit their interests and educational needs, based on their profiles and
the submitted course reviews (Aciar et al. 2006; Terzi et al. 2011).
Acknowledgments
The author is grateful to Anindya Ghose, Natalia Levina, Vilma Todri, and Alexander Tuzhilin as well as
the track chairs, associate editors, and three anonymous reviewers at the International Conference on
Information Systems (ICIS 2013) for their constructive comments and valuable feedback on an earlier
version of the manuscript.
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