This document summarizes a presentation on data science in education given by Dr. Liu Ming-chi from National Cheng Kung University. The presentation outlines include topics such as data science, data scientists, the PISA assessment, future skills needed in education, how AI could impact education, and cases studies. It also includes several links to videos and articles about topics relating to using data in education.
The role of institutional data in Learning AnalyticsAbelardo Pardo
Learning analytics has the potential of improving how higher education institutions operate. A significant portion of this potential derives from the use of institutional data. In this talk we review the role of these units in achieving institutional capacity and show some examples of the type of solutions possible at the level of instructors.
Generating Actionable Predictive Models of Academic PerformanceAbelardo Pardo
Exploring predictive models that are closer to action by instructors. The talk proposes the use of hierarchical partitioning algorithms to produce decision trees that can be used to divide students into groups and simplify how feedback is provided.
The role of data in the provision of feedback at scaleAbelardo Pardo
The abundance of data in learning environments poses both a potential and a challenge. Improvements in the student experience need a strong connection between data, learning design and the delivery platform. In this talk we explore some ideas on how to establish this connection with respect to feedback.
The role of data in the provision of feedback at scaleAbelardo Pardo
Technology mediation allows to capture comprehensive data sets about interactions occurring in learning experiences. Although these data sets have the potential of increasing the insight on how learning occurs, their use strongly depends on two aspects: the data has to be properly situated in the learning design, and the insights derived need to be translated into actions. In this talk we will explore how to establish this connection for the case of the provision of feedback. We will approach the problem from the point of view of intelligence amplification, that is, how data can support instructors to provide better support to learners through feedback. The talk will discuss some preliminary results from the Ontasklearning.org project.
Successful transition from secondary to higher education using learning analy...Tinne De Laet
This document discusses a workshop on using learning analytics to support students' transition from secondary to higher education. It introduces the STELA project, which aims to apply learning analytics beyond identifying at-risk students, to provide inclusive and actionable feedback for all students. The workshop agenda includes an introduction to learning analytics, discussion of how it could help with the school transition, and presentation of the STELA project ideas focusing on academic performance, engagement, skills, and well-being. Groups will discuss the project ideas and present their feedback.
Feedback at scale with a little help of my algorithmsAbelardo Pardo
Talk exploring how to use data to provide scalable feedback in learning experiences. The solutions explored propose the use of algorithms to enhance how humans instructors provide feedback to students more effectively
This document summarizes a presentation on data science in education given by Dr. Liu Ming-chi from National Cheng Kung University. The presentation outlines include topics such as data science, data scientists, the PISA assessment, future skills needed in education, how AI could impact education, and cases studies. It also includes several links to videos and articles about topics relating to using data in education.
The role of institutional data in Learning AnalyticsAbelardo Pardo
Learning analytics has the potential of improving how higher education institutions operate. A significant portion of this potential derives from the use of institutional data. In this talk we review the role of these units in achieving institutional capacity and show some examples of the type of solutions possible at the level of instructors.
Generating Actionable Predictive Models of Academic PerformanceAbelardo Pardo
Exploring predictive models that are closer to action by instructors. The talk proposes the use of hierarchical partitioning algorithms to produce decision trees that can be used to divide students into groups and simplify how feedback is provided.
The role of data in the provision of feedback at scaleAbelardo Pardo
The abundance of data in learning environments poses both a potential and a challenge. Improvements in the student experience need a strong connection between data, learning design and the delivery platform. In this talk we explore some ideas on how to establish this connection with respect to feedback.
The role of data in the provision of feedback at scaleAbelardo Pardo
Technology mediation allows to capture comprehensive data sets about interactions occurring in learning experiences. Although these data sets have the potential of increasing the insight on how learning occurs, their use strongly depends on two aspects: the data has to be properly situated in the learning design, and the insights derived need to be translated into actions. In this talk we will explore how to establish this connection for the case of the provision of feedback. We will approach the problem from the point of view of intelligence amplification, that is, how data can support instructors to provide better support to learners through feedback. The talk will discuss some preliminary results from the Ontasklearning.org project.
Successful transition from secondary to higher education using learning analy...Tinne De Laet
This document discusses a workshop on using learning analytics to support students' transition from secondary to higher education. It introduces the STELA project, which aims to apply learning analytics beyond identifying at-risk students, to provide inclusive and actionable feedback for all students. The workshop agenda includes an introduction to learning analytics, discussion of how it could help with the school transition, and presentation of the STELA project ideas focusing on academic performance, engagement, skills, and well-being. Groups will discuss the project ideas and present their feedback.
Feedback at scale with a little help of my algorithmsAbelardo Pardo
Talk exploring how to use data to provide scalable feedback in learning experiences. The solutions explored propose the use of algorithms to enhance how humans instructors provide feedback to students more effectively
Factors affecting the quick completion of project research by bachelor of edu...Alexander Decker
This study investigated factors affecting the quick completion of project research by Bachelor of Education students studying through distance learning at Alvan Ikoku Federal College of Education in Nigeria. A questionnaire was administered to 150 students to understand challenges in completing their research projects. The results found that many students struggled with statistical analysis and lacked assistance from supervisors. It was recommended that institutions improve statistical training for distance students, ensure supervisors are more engaged, and extend the duration allowed for research projects. Overall, the study aimed to understand difficulties distance students face in completing required research projects in order to improve support systems.
This document discusses learning analytics, which involves measuring, retrieving, collecting, and analyzing student data from various learning environments. Learning analytics can help educators track student progress and behavior to improve instruction and support. However, there are also challenges around data storage, privacy, and ensuring analytics are aligned with educational goals. Opportunities exist to capture more detailed behavioral data through tools, but institutions must have the capacity to maintain analytics systems and apply insights pedagogically.
ASCILITE Webinar: A review of five years of implementation and research in al...Bart Rienties
Date and time: Wednesday 20 September 2017 at 5pm AEST
Abstract: The Open University UK (OU) has been one of few institutions that have explicitly and systematically captured the designs for learning at a large scale. By applying advanced analytical techniques on large and fine-grained datasets, we have been unpacking the complexity of instructional practices, as well as providing empirical evidence of how learning designs influence student behaviour, satisfaction, and performance. This seminar will discuss the implementation of learning design at the OU in the last 5 years, and reviews empirical evidence from several studies that have linked learning design with learning analytics. Recommendations are put forward to support future adoptions of the learning design approach, and potential research trajectories.
https://ascilite.org/get-involved/sigs/learning-analytics-sig/
www.bartrienties.nl
AI in Education Amsterdam Data Science (ADS) What have we learned after a dec...Bart Rienties
The Open University UK (OU) has been implementing learning analytics and learning design on a large scale since 2012. With its 170+ students and 4000+ teaching staff, the OU has been at the forefront of testing, implementing, and evaluating the impact of learning analytics and learning design on students outcome and retention. A range of reviews and scholarly repositories (e.g., Web of Science) indicate that the OU is the largest contributor to academic output in learning analytics and learning design in the world. However, despite the large uptake of learning analytics at the OU there are a range of complex issues in terms of buy-in from staff, data infrastructures, ethics and privacy, student engagement, and perhaps most importantly how to make sense of big and small data in a complex organisation like the OU. During his talk Bart will be presenting on the implementation and learnings.
Using Learning analytics to support learners and teachers at the Open UniversityBart Rienties
In this seminar Prof Bart Rienties will reflect on how the Open University UK has become a leading institution in implementing learning analytics at scale amongst its 170K students and 5K staff. Furthermore, he will discuss how learning analytics is being adopted at other UK institutions, and what the implications for higher education might be in these Covid19 times.
https://www.kent.ac.uk/cshe/news-events.html
Presentation Slides from ISSOTL 2015.
Bronnimann, J., West, D., Heath, D. & Huijser, H. (2015) Leveraging learning analytics for future pedagogies and scholarship. Paper presented at Leading learning and the scholarship of change: 12th annual ISSOTL conference, Melbourne, Australia.
The document discusses ten trends currently affecting the field of instructional design and technology. It describes how the definitions and scope of the field have expanded over time to include a more systematic approach and use of instructional media. Key trends discussed include performance improvement, knowledge management, electronic performance support systems, increased e-learning, learning objects, and recognition of informal learning.
Smartphone for Academic Purpose: Assessing It's Adoption By Business Students...Ahmed Aliyu Palladan, PhD
This document reports on a study that assessed the adoption of smartphones by business students in Nigeria for academic purposes. It used the Technology Acceptance Model (TAM) as the conceptual framework. A survey was administered to 172 business students across three universities. The findings were:
1) Perceived behavior of smartphone use was positively related to intention to use smartphones for academic activities, supporting one hypothesis.
2) Perceived usefulness of smartphones was also positively related to intention to use, supporting another hypothesis.
3) However, perceived ease of use was not found to be significantly related to intention to use, contrary to what TAM predicts. This finding has also been observed in some prior studies.
Exploring hands-on multidisciplinary STEM with Arduino EsploraAbelardo Pardo
In this presentation we describe the Madmaker project. The use of Arduino Esplora to promote STEM activities in High Schools. It contains a description of our approach and data derived from the evaluation.
Comparative Study of Different Approaches for Measuring Difficulty Level of Q...ijtsrd
"Semantics based information representations such as ontologies are found to be very useful in repeatedly generating important factual questions. Formative the difficulty level Of these system generated questions is helpful to successfully make use of them in various learning and specialized applications. The accessible approaches for result the difficulty level of factual questions are very simple and are limited to a few basic principles. We suggest a new tactic for this problem by considering an edifying theory called Item Response Theory IRT . In the IRT, facts skill of end users learners are considered for assigning difficulty levels, because of the assumptions that a given question is apparent differently by learners of various proficiencies. We have done a detailed study on the features factors of a question statement which could perhaps determine its difficulty level for three learner categories experts, intermediates, and easy . Ayesha Pathan | Dr. Pravin Futane ""Comparative Study of Different Approaches for Measuring Difficulty Level of Question"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd21532.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/21532/comparative-study-of-different-approaches-for-measuring-difficulty-level-of-question/ayesha-pathan"
Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning. The document discusses motivation and goals of learning analytics, challenges, and examples of data that could be analyzed from students, including demographics, academic performance, physical behavior, and online behavior. It also discusses ensuring principles of transparency, alignment with pedagogy, and responsibility in student data use. Examples are provided of diagnostic testing, analyzing VLE access data, and measuring the effects of video use and flipped classrooms.
Inaugural lecture: The power of learning analytics to give students (and teac...Bart Rienties
Join us at the Berrill Theatre and online on Tuesday 30 January 2018, 6-7pm for the Inaugural Lecture of Professor Bart Rienties, in which he will talk about the power of learning analytics in teaching and learning. Bart Rienties is Professor of Learning Analytics at the Institute of Educational Technology (IET) at The Open University. He is programme director Learning Analytics within IET and head of Data Wranglers, whereby he leads of group of learning analytics academics who conduct evidence-based research and sense making of Big Data at the OU.
As educational psychologist, he conducts multi-disciplinary research on work-based and collaborative learning environments and focuses on the role of social interaction in learning, which is published in leading academic journals and books. His primary research interests are focussed on Learning Analytics, Computer-Supported Collaborative Learning, and the role of motivation in learning. Furthermore, Bart is interested in broader internationalisation aspects of higher education. He has successfully led a range of institutional/national/European projects and received a range of awards for his educational innovation projects.
Bart is World Champion Transplant cycling Team Time Trial 2017, the first academic with a transplant to be promoted to full professor, and a keen explorer of life.
In The power of learning analytics to give students (and teachers) what they want!, Bart will describe how his research into learning analytics is enabling him to predict which learning strategy might work best for each student, and provide different, unique experiences for each depending on what they want. In particular, he will explore how student dispositions like motivation, emotion, or anxiety encourages or hinders effective online learning, and how we may need to adjust our approaches depending on individual differences.
Event programme:
18:00 - 18:45 – The power of learning analytics to give students (and teachers) what they want!
18:45 - 19:00 – Q&A
19:00 - 19:45 – Drinks Reception
There will be time for questions and comments. We very much hope you will be able to attend what promises to be an inspiring event and have your say.
[Extended] Bottom-up growth of learning analytics at two Australian universit...Danny Liu
Presented at the University of New South Wales Learning Analytics and Educational Data Science research group meeting, April 2016.
This presentation will outline two approaches to learning analytics at the University of Sydney and Macquarie University, where staff are closely involved in the coevolution and development of two bespoke learning analytics tools to personalise student-staff interactions at scale. The University of Sydney system, called the Student Relationship Engagement System (SRES), is a highly-customisable web-based tool that supports the efficient capture and collation of student datasets. A companion mobile app helps staff quickly collect and access student data. Through an embedded messaging system, teaching staff can set up fully customisable rules to contact students via personalised emails and text messages. A nascent feature allows staff to leverage machine learning to uncover hidden patterns and relationships within and between datasets. The Macquarie University system is an enhancement of an existing Moodle plug-in, the Moodle Engagement Analytics Plugin (MEAP). MEAP can readily access data on student assessments, completions, login activity, forum activity, and the gradebook, amongst others, which are customisably represented as ‘risk indicators’. MEAP allows flexible and customisable interrogation of these data, and provides staff the ability to send personalised emails to students based on these risk indicators. At both institutions, these learning analytics approaches have grown from the grassroots to address pressing staff needs, highlighting the importance of this bespoke coevolution process of design, development, and implementation. The systems have enjoyed substantial organic adoption and are associated with positive student outcomes. As open source developments, we are very interested in working together to open up accessible learning analytics to teachers and students.
The power of learning analytics to measure learning gains: an OU, Surrey and ...Bart Rienties
Learning gains has increasingly become apparent within the HE literature, gained traction in government policies in the UK, and are at the heart of Teaching Excellence Framework (TFL). As such, this raises a question to what extent teaching and learning environment can actually predict students’ learning gains using principles of learning analytics. In this presentation, which is joined work with University of Surrey and Oxford Brookes, I will focus on some preliminary findings based upon developing and testing an Affective-Behaviour-Cognition learning gains model using longitudinal approach. The main aim of the research is to examine whether learning gains occur on all three levels of Affective-Behaviour-Cognition model and whether any particular student or course characteristics can predict learning gains or lack of learning and dropout. For more info, see https://abclearninggains.com/
The document summarizes recent developments in technology-supported assessment of self-regulated learning (SRL). It describes widely used self-report survey methods like the Motivated Strategies for Learning Questionnaire and innovative trace-based methods that can automatically collect data on students' learning processes. Trace measures analyze students' online behaviors like note-taking or help seeking. Model tracing matches students' actions to cognitive models of SRL. The computerization of self-evaluation requires students to self-report and compare reports to actual performance. The review finds both established and emerging methods provide new opportunities to understand SRL with technology.
This document summarizes research on using student response systems and interactive whiteboards to support effective instruction. It finds that student response systems can promote learning and engagement when coupled with appropriate instructional strategies like formative assessment and questioning. Research also indicates interactive whiteboards increase student engagement through interactive lessons. The eInstruction Insight 360 system incorporates these technologies to support practices shown to improve learning, such as providing feedback, addressing misconceptions, and modifying teaching in response to student understanding.
In this talk we will analyze the effects of gamification in the social network of a large online course on ‘digital skills for teachers.’ Educational social networking websites and learning systems can gather information about contributions of participants and about the underlying social network. We will present an experimental gamification layer with three game elements (points, badges, and leaderboard) that was delivered to students. Social network analysis (SNA) and principal component analysis (PCA) can then be used to analyze the differences between groups using information about contributions to the website, and position and influence in the social network of each participant. Initial results suggest that variables and participants group differently, and that gamification may influence the structure of the social network of participants in the course. The first component (F1) can be a good descriptor of students’ work and position in the network that can be used to build predictive models of learning success. The models suggest that the probability of passing the course increases more rapidly in the experimental (gamified) groups for students that participate.
Articulating the connection between Learning Design and Learning AnalyticsAbelardo Pardo
Learning analytics is a discipline that uses data captured by technology during a learning experience to increase our level of understanding, increase its quality, and improve the environment in which it occurs. But these experiences need to be designed first. In this talk we start from the statement that there is no such thing as a neutral design. In the era of increasing technology mediation Learning experiences need to be designed considering the capacity to capture data, the possibility of making sense and derive knowledge from the data, and the need to act on that knowledge. In this talk we will explore some initiatives to make these connections explicit in a learning design. Using a flipped learning experience, we will explore how to embed data and data analysis as part of the design tasks.
Factors affecting the quick completion of project research by bachelor of edu...Alexander Decker
This study investigated factors affecting the quick completion of project research by Bachelor of Education students studying through distance learning at Alvan Ikoku Federal College of Education in Nigeria. A questionnaire was administered to 150 students to understand challenges in completing their research projects. The results found that many students struggled with statistical analysis and lacked assistance from supervisors. It was recommended that institutions improve statistical training for distance students, ensure supervisors are more engaged, and extend the duration allowed for research projects. Overall, the study aimed to understand difficulties distance students face in completing required research projects in order to improve support systems.
This document discusses learning analytics, which involves measuring, retrieving, collecting, and analyzing student data from various learning environments. Learning analytics can help educators track student progress and behavior to improve instruction and support. However, there are also challenges around data storage, privacy, and ensuring analytics are aligned with educational goals. Opportunities exist to capture more detailed behavioral data through tools, but institutions must have the capacity to maintain analytics systems and apply insights pedagogically.
ASCILITE Webinar: A review of five years of implementation and research in al...Bart Rienties
Date and time: Wednesday 20 September 2017 at 5pm AEST
Abstract: The Open University UK (OU) has been one of few institutions that have explicitly and systematically captured the designs for learning at a large scale. By applying advanced analytical techniques on large and fine-grained datasets, we have been unpacking the complexity of instructional practices, as well as providing empirical evidence of how learning designs influence student behaviour, satisfaction, and performance. This seminar will discuss the implementation of learning design at the OU in the last 5 years, and reviews empirical evidence from several studies that have linked learning design with learning analytics. Recommendations are put forward to support future adoptions of the learning design approach, and potential research trajectories.
https://ascilite.org/get-involved/sigs/learning-analytics-sig/
www.bartrienties.nl
AI in Education Amsterdam Data Science (ADS) What have we learned after a dec...Bart Rienties
The Open University UK (OU) has been implementing learning analytics and learning design on a large scale since 2012. With its 170+ students and 4000+ teaching staff, the OU has been at the forefront of testing, implementing, and evaluating the impact of learning analytics and learning design on students outcome and retention. A range of reviews and scholarly repositories (e.g., Web of Science) indicate that the OU is the largest contributor to academic output in learning analytics and learning design in the world. However, despite the large uptake of learning analytics at the OU there are a range of complex issues in terms of buy-in from staff, data infrastructures, ethics and privacy, student engagement, and perhaps most importantly how to make sense of big and small data in a complex organisation like the OU. During his talk Bart will be presenting on the implementation and learnings.
Using Learning analytics to support learners and teachers at the Open UniversityBart Rienties
In this seminar Prof Bart Rienties will reflect on how the Open University UK has become a leading institution in implementing learning analytics at scale amongst its 170K students and 5K staff. Furthermore, he will discuss how learning analytics is being adopted at other UK institutions, and what the implications for higher education might be in these Covid19 times.
https://www.kent.ac.uk/cshe/news-events.html
Presentation Slides from ISSOTL 2015.
Bronnimann, J., West, D., Heath, D. & Huijser, H. (2015) Leveraging learning analytics for future pedagogies and scholarship. Paper presented at Leading learning and the scholarship of change: 12th annual ISSOTL conference, Melbourne, Australia.
The document discusses ten trends currently affecting the field of instructional design and technology. It describes how the definitions and scope of the field have expanded over time to include a more systematic approach and use of instructional media. Key trends discussed include performance improvement, knowledge management, electronic performance support systems, increased e-learning, learning objects, and recognition of informal learning.
Smartphone for Academic Purpose: Assessing It's Adoption By Business Students...Ahmed Aliyu Palladan, PhD
This document reports on a study that assessed the adoption of smartphones by business students in Nigeria for academic purposes. It used the Technology Acceptance Model (TAM) as the conceptual framework. A survey was administered to 172 business students across three universities. The findings were:
1) Perceived behavior of smartphone use was positively related to intention to use smartphones for academic activities, supporting one hypothesis.
2) Perceived usefulness of smartphones was also positively related to intention to use, supporting another hypothesis.
3) However, perceived ease of use was not found to be significantly related to intention to use, contrary to what TAM predicts. This finding has also been observed in some prior studies.
Exploring hands-on multidisciplinary STEM with Arduino EsploraAbelardo Pardo
In this presentation we describe the Madmaker project. The use of Arduino Esplora to promote STEM activities in High Schools. It contains a description of our approach and data derived from the evaluation.
Comparative Study of Different Approaches for Measuring Difficulty Level of Q...ijtsrd
"Semantics based information representations such as ontologies are found to be very useful in repeatedly generating important factual questions. Formative the difficulty level Of these system generated questions is helpful to successfully make use of them in various learning and specialized applications. The accessible approaches for result the difficulty level of factual questions are very simple and are limited to a few basic principles. We suggest a new tactic for this problem by considering an edifying theory called Item Response Theory IRT . In the IRT, facts skill of end users learners are considered for assigning difficulty levels, because of the assumptions that a given question is apparent differently by learners of various proficiencies. We have done a detailed study on the features factors of a question statement which could perhaps determine its difficulty level for three learner categories experts, intermediates, and easy . Ayesha Pathan | Dr. Pravin Futane ""Comparative Study of Different Approaches for Measuring Difficulty Level of Question"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd21532.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/21532/comparative-study-of-different-approaches-for-measuring-difficulty-level-of-question/ayesha-pathan"
Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning. The document discusses motivation and goals of learning analytics, challenges, and examples of data that could be analyzed from students, including demographics, academic performance, physical behavior, and online behavior. It also discusses ensuring principles of transparency, alignment with pedagogy, and responsibility in student data use. Examples are provided of diagnostic testing, analyzing VLE access data, and measuring the effects of video use and flipped classrooms.
Inaugural lecture: The power of learning analytics to give students (and teac...Bart Rienties
Join us at the Berrill Theatre and online on Tuesday 30 January 2018, 6-7pm for the Inaugural Lecture of Professor Bart Rienties, in which he will talk about the power of learning analytics in teaching and learning. Bart Rienties is Professor of Learning Analytics at the Institute of Educational Technology (IET) at The Open University. He is programme director Learning Analytics within IET and head of Data Wranglers, whereby he leads of group of learning analytics academics who conduct evidence-based research and sense making of Big Data at the OU.
As educational psychologist, he conducts multi-disciplinary research on work-based and collaborative learning environments and focuses on the role of social interaction in learning, which is published in leading academic journals and books. His primary research interests are focussed on Learning Analytics, Computer-Supported Collaborative Learning, and the role of motivation in learning. Furthermore, Bart is interested in broader internationalisation aspects of higher education. He has successfully led a range of institutional/national/European projects and received a range of awards for his educational innovation projects.
Bart is World Champion Transplant cycling Team Time Trial 2017, the first academic with a transplant to be promoted to full professor, and a keen explorer of life.
In The power of learning analytics to give students (and teachers) what they want!, Bart will describe how his research into learning analytics is enabling him to predict which learning strategy might work best for each student, and provide different, unique experiences for each depending on what they want. In particular, he will explore how student dispositions like motivation, emotion, or anxiety encourages or hinders effective online learning, and how we may need to adjust our approaches depending on individual differences.
Event programme:
18:00 - 18:45 – The power of learning analytics to give students (and teachers) what they want!
18:45 - 19:00 – Q&A
19:00 - 19:45 – Drinks Reception
There will be time for questions and comments. We very much hope you will be able to attend what promises to be an inspiring event and have your say.
[Extended] Bottom-up growth of learning analytics at two Australian universit...Danny Liu
Presented at the University of New South Wales Learning Analytics and Educational Data Science research group meeting, April 2016.
This presentation will outline two approaches to learning analytics at the University of Sydney and Macquarie University, where staff are closely involved in the coevolution and development of two bespoke learning analytics tools to personalise student-staff interactions at scale. The University of Sydney system, called the Student Relationship Engagement System (SRES), is a highly-customisable web-based tool that supports the efficient capture and collation of student datasets. A companion mobile app helps staff quickly collect and access student data. Through an embedded messaging system, teaching staff can set up fully customisable rules to contact students via personalised emails and text messages. A nascent feature allows staff to leverage machine learning to uncover hidden patterns and relationships within and between datasets. The Macquarie University system is an enhancement of an existing Moodle plug-in, the Moodle Engagement Analytics Plugin (MEAP). MEAP can readily access data on student assessments, completions, login activity, forum activity, and the gradebook, amongst others, which are customisably represented as ‘risk indicators’. MEAP allows flexible and customisable interrogation of these data, and provides staff the ability to send personalised emails to students based on these risk indicators. At both institutions, these learning analytics approaches have grown from the grassroots to address pressing staff needs, highlighting the importance of this bespoke coevolution process of design, development, and implementation. The systems have enjoyed substantial organic adoption and are associated with positive student outcomes. As open source developments, we are very interested in working together to open up accessible learning analytics to teachers and students.
The power of learning analytics to measure learning gains: an OU, Surrey and ...Bart Rienties
Learning gains has increasingly become apparent within the HE literature, gained traction in government policies in the UK, and are at the heart of Teaching Excellence Framework (TFL). As such, this raises a question to what extent teaching and learning environment can actually predict students’ learning gains using principles of learning analytics. In this presentation, which is joined work with University of Surrey and Oxford Brookes, I will focus on some preliminary findings based upon developing and testing an Affective-Behaviour-Cognition learning gains model using longitudinal approach. The main aim of the research is to examine whether learning gains occur on all three levels of Affective-Behaviour-Cognition model and whether any particular student or course characteristics can predict learning gains or lack of learning and dropout. For more info, see https://abclearninggains.com/
The document summarizes recent developments in technology-supported assessment of self-regulated learning (SRL). It describes widely used self-report survey methods like the Motivated Strategies for Learning Questionnaire and innovative trace-based methods that can automatically collect data on students' learning processes. Trace measures analyze students' online behaviors like note-taking or help seeking. Model tracing matches students' actions to cognitive models of SRL. The computerization of self-evaluation requires students to self-report and compare reports to actual performance. The review finds both established and emerging methods provide new opportunities to understand SRL with technology.
This document summarizes research on using student response systems and interactive whiteboards to support effective instruction. It finds that student response systems can promote learning and engagement when coupled with appropriate instructional strategies like formative assessment and questioning. Research also indicates interactive whiteboards increase student engagement through interactive lessons. The eInstruction Insight 360 system incorporates these technologies to support practices shown to improve learning, such as providing feedback, addressing misconceptions, and modifying teaching in response to student understanding.
In this talk we will analyze the effects of gamification in the social network of a large online course on ‘digital skills for teachers.’ Educational social networking websites and learning systems can gather information about contributions of participants and about the underlying social network. We will present an experimental gamification layer with three game elements (points, badges, and leaderboard) that was delivered to students. Social network analysis (SNA) and principal component analysis (PCA) can then be used to analyze the differences between groups using information about contributions to the website, and position and influence in the social network of each participant. Initial results suggest that variables and participants group differently, and that gamification may influence the structure of the social network of participants in the course. The first component (F1) can be a good descriptor of students’ work and position in the network that can be used to build predictive models of learning success. The models suggest that the probability of passing the course increases more rapidly in the experimental (gamified) groups for students that participate.
Articulating the connection between Learning Design and Learning AnalyticsAbelardo Pardo
Learning analytics is a discipline that uses data captured by technology during a learning experience to increase our level of understanding, increase its quality, and improve the environment in which it occurs. But these experiences need to be designed first. In this talk we start from the statement that there is no such thing as a neutral design. In the era of increasing technology mediation Learning experiences need to be designed considering the capacity to capture data, the possibility of making sense and derive knowledge from the data, and the need to act on that knowledge. In this talk we will explore some initiatives to make these connections explicit in a learning design. Using a flipped learning experience, we will explore how to embed data and data analysis as part of the design tasks.
1. The document discusses various applications of artificial neural networks (ANNs) such as pattern classification, clustering, forecasting, association, and summarization of news articles.
2. It provides examples of how ANNs can be used to classify images and documents into different groups or events. The architecture of a multi-document news summarization system using ANNs is shown.
3. The biological mechanisms of neural networks in the human brain are compared with artificial neural networks. Examples of different activation functions in artificial neurons and learning algorithms like the perceptron are presented.
This document provides an overview of the American Psychological Association (APA) style for citing sources and formatting manuscripts. It discusses the key chapters and content covered in the APA manual. The presentation emphasizes creating a unified format to focus on content, and introduces the APA author-date citation style. It also covers formatting reference lists, citing different source types like books, book chapters, journal articles, and webpages. Common software for managing references and citing sources while writing are introduced.
在這個資料科學蔚為風潮的年代,身為一個對新技術充滿好奇的攻城獅,自然會想要擴充自己的武器庫,學習嶄新的資料分析工具;而 R 語言,一個由統計學家專門為了資料探索與分析所開發的腳本語言,具有龐大的開源社群支持以及琳瑯滿目、數以萬計的各式套件,正是當今學習資料科學相關工具的首選。
然而,R 語言的設計邏輯與一般的程式語言不同,工程師們過去學習程式語言的經驗,往往造成學習 R 語言的障礙,本課程將從 R 語言的基礎開始,讓同學們從課堂講解以及互動式上機課程中,得以徹底理解 R 語言的核心概念與精要,學習如何利用 R 語言問資料問題,並且從資料分析的角度撰寫效率良好同時具有高度可讀性的 R 語言代碼。
This document discusses applying data mining techniques to analyze active users on Reddit. It defines active users as those who posted or commented in at least 5 subreddits and have at least 5 posts/comments in each subreddit. The preprocessing steps extract over 25,000 active users and their posts from the raw Reddit data. K-means clustering is then used to cluster the active users into 10 groups based on their activities to gain insights into different types of active users on Reddit.
在此課程中將帶領對資料分析感到陌生卻又充滿興趣的您,完整地學會運用 R 語言從最初的蒐集資料、探索性分析解讀資料,並進行文字探勘,發現那些肉眼看不見、隱藏在資料底下的意義。此課程主要設計給對於 R 語言有基本認識,想要進一步熟悉實作分析的朋友們,希望在課程結束後,您能夠更熟悉 R 語言這個豐富的分析工具。透過蘋果日報慈善捐款的資料集,了解如何從頭解析網頁,撰寫爬蟲自動化收集資訊;取得資料後,能夠靈活處理資料,做清洗、整合及探索;並利用現成的套件進行文字探勘、文本解析;我們將一步步實際走一回資料分析的歷程,處理、觀察、解構資料,試著看看人們在捐款的決策過程中,究竟是什麼因素產生了影響,以及這些結果又是如何從資料中挖掘而出的呢?
This document provides an introduction to exploring and visualizing data using the R programming language. It discusses the history and development of R, introduces key R packages like tidyverse and ggplot2 for data analysis and visualization, and provides examples of reading data, examining data structures, and creating basic plots and histograms. It also demonstrates more advanced ggplot2 concepts like faceting, mapping variables to aesthetics, using different geoms, and combining multiple geoms in a single plot.
(1) This document provides a quick tour of machine learning concepts including the components, types, and step-by-step process of machine learning.
(2) It discusses machine learning applications in areas like credit approval, education, recommender systems, and reinforcement learning.
(3) The tour outlines the key components of a machine learning problem including the target function, training data, learning algorithm, hypothesis set, and learned hypothesis. It also distinguishes between supervised, unsupervised, and semi-supervised learning problems.
2nd Regional Symposium on Open Educational Resources:
Beyond Advocacy, Research and Policy
24 – 27 June 2014
Sub-theme 5: Quality
Concepts and Measurements
Mehwish Waheed, Kiran Kaur
Virtual hrd and ePortfolios: Higher Education to Workforce ContinuumElisabethEBennett
Presented at AAEEBL July 2012, this presentation discusses ePortofolios as a microcosm of Virtual HRD. Medical Education is used as an example of the continuum. Conclusions include ethical issues and potential pitfalls when ePortfolios are used for multiple purposes for multiple stakeholders.
Calidad en formación médica continuada. EuroCAT ergonomic analysisCRISEL BY AEFOL
- The document discusses quality electronic continuing medical education (eCME) and how it can help meet physicians' lifelong learning needs through updated content, personalized instruction, and interactive learning.
- It addresses how to measure the quality of an eCME activity based on addressing physicians' needs, efficiency, and accreditation standards.
- Five key criteria for quality eCME are presented: accredited activities, instructional design, use of multimedia resources, providing rapid feedback, and adherence to quality standards.
Advantages and Disadvantages of the Objective Structured Clinical Examination...ijtsrd
This document summarizes a literature review on the advantages and disadvantages of using Objective Structured Clinical Examinations (OSCEs) in nursing education. The review found that OSCEs are an effective way to evaluate students' clinical skills and have grown in use over the last decade. However, OSCEs also have several disadvantages, such as being resource-intensive, expensive, and difficult to implement. While OSCEs provide benefits like objectivity and preparing students for practice, there are obstacles that limit their applicability in some contexts. Overall, the literature indicates that OSCEs are useful for nursing education when implemented appropriately, but other assessment methods may need to be used in combination due to the challenges of OSCEs
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Star Model Research
The Star Model provides a focal point for evidence-based practice activities, including education and interdisciplinary research projects. Projects and investigations are concentrated on two objectives: (a) basic and professional level workforce development for EBP; and (b) the study of the processes and outcomes within evidence-based quality improvement. We study evidence synthesis, translation of evidence into practice, and healthcare provider and organizational change. A short description of representative projects and research follows. Current Research TopicsList of TopicsStar Model Translational Research
This ongoing program expands and tests a model for understanding evidence-based practice. The study of EBP is essentially the study of transforming knowledge produced through primary studies and moving it through adoption into clinical decision-making.
Using the Star Model as a framework, our program of translational research investigates phenomena associated with EBP, including summarizing evidence, clinical guideline development and uptake, organizational culture, and outcome measures. The initial project in this timely program of research was Evaluation of Systematic Reviews Published in Nursing Literature: A Replication, that pointed to the need for more rigorous systematic reviews in nursing. Subsequent projects investigate factors associated with uptake of clinical practice guidelines, innovation, and system culture change.
Top of TopicsStar Model of Knowledge Transformation
Developed in 2004, the Star Model is configured as a simple 5-point star; and it explains how knowledge is transformed at five major stages, starting from primary research, and continuing through the stages of evidence summary, translation, integration, and evaluation. This model places nursing’s previous scientific work within the context of EBP and is proving useful for examining the EBP process, roles in EBP, and research methods with which to investigate EBP.
Adopted by scores of hospitals across the nation as part of their journey to excellence, the Star Model forms a foundation for developing workforce competencies, organizing projects, and employing EBP in clinical settings. Influenced by Imogene King, we continue to evolve the Star Model as a theory, combining concepts of knowledge transformation with elements of communication, mutual goal setting, and systems theory.
Top of TopicsImprovement Science Research Network
While quality improvement activities are highly encouraged in acute care settings, hospitals and improvement scientists are not well connected. Th.
Walden University
NURS 6050 Policy and Advocacy for Improving Population Health
Module 3
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IntroductionResourcesDiscussionAssignment☰Menu× NURS 6050 Policy and Advocacy for Improving Population Health Back to Course Home Course Calendar Syllabus Course Information Resource List Support, Guidelines, and Policies Module 1 Module 2 Module 3 Module 4 Module 5 Module 6
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Photo Credit: Getty Images/iStockphotoModule 3: Regulation (Weeks 5-6)
Laureate Education (Producer). (2018). Regulation [Video file]. Baltimore, MD: Author.
Rubic_Print_FormatCourse CodeClass CodeAssignment TitleTotal PointsLDR-463LDR-463-O501Topic 5 Journal Entry30.0CriteriaPercentageUnsatisfactory (0.00%)Less Than Satisfactory (65.00%)Satisfactory (75.00%)Good (85.00%)Excellent (100.00%)CommentsPoints EarnedContent100.0%Response to Journal Entry Prompt80.0%Response to the journal entry prompt is not present.Response to the journal entry prompt is incomplete or incorrect.Response to the journal entry prompt is complete but lacks relevant detail.Response to the journal entry prompt is thorough and contains substantial supporting details.Response to the journal entry prompt is complete and contains relevant supporting details.Mechanics of Writing includes spelling, punctuation, grammar, and language use.20.0%Frequent and repetitive mechanical errors distract the reader. Inconsistencies in language choice (register) or word choice are present. Sentence structure is correct but not varied.Surface errors are pervasive enough that they impede communication of meaning. Inappropriate word choice or sentence construction is used.Some mechanical errors or typos are present, but they are not overly distracting to the reader. Correct and varied sentence structure and audience-appropriate language are employed.Prose is largely free of mechanical errors, although a few may be present. The writer uses a variety of effective sentence structures and figures of speech.Writer is clearly in command of standard, written, academic English.Total Weightage100%
Walden University
NURS 6050 Policy and Advocacy for Improving Population Health ...
This document describes a theory-based continuing interprofessional education (CIPE) program designed to improve sepsis care through enhanced healthcare team collaboration. The program involved three activities over six months that applied social identity theory, reflective and experiential learning theory, and communities of practice theory. Evaluation results found the program positively changed provider perceptions of team-based care and increased commitment to collaborative behaviors. Participants demonstrated a greater appreciation of other roles in sepsis care.
An Inquiry on the Self-Esteem and Self-Efficacy Level of Information Technolo...IJAEMSJORNAL
This study aimed to identify, analyze and determine the level of self-efficacy and self-esteem of B.S. Information Technology (BSIT) students of a higher learning institution in Nueva Ecija, Philippines. It was conducted during the 1st Semester of the academic year 2019-2020. This research utilized descriptive approach to describe the level of self-esteem and self-efficacy of the students and to draw valuable insights that may contribute to the improvement of the teaching and learning practices of the faculty members in the college. The researchers used random sampling to ensure that all year levels are well represented in the study. There were 285 students who voluntarily responded after the researchers explained to them the purpose of this study. Responses were tallied, summarized and interpreted. Results show that the level of self-esteem and self-efficacy of the students were moderate/medium (WM=2.03, WM=2.08). This indicates that depending on the given situation or context, students may increase or decrease the level of their self-esteem and self-efficacy. This study suggest that students may be exposed to more activities that may help them improve their self-esteem and self-efficacy to greatly contribute to their holistic development. Future studies may be conducted to a larger number of respondents and to understand the link between self-efficacy and self-esteem on their academic performance, drop-out rates, and retention rates.
Introduction of Objective Structured Clinical Examination as assessment tool ...iosrjce
This document describes a study that introduced Objective Structured Clinical Examinations (OSCEs) as an assessment tool in formative examinations for the Dermatology, Venereology and Leprology department at a medical college in India. The study aimed to assess the feasibility and acceptability of OSCEs by students and faculty. Students and faculty were oriented to OSCEs and then 15 stations were used to assess students' clinical skills over 15 days. Feedback found that over 90% of students and faculty found OSCEs acceptable, feasible, improved clinical skills, and were better than conventional assessment methods. The study concluded that introducing OSCEs increased reliable assessment and student confidence in clinical skills.
This study piloted the use of surveys to measure third-year undergraduate nursing students' reflective thinking skills and critical reflection self-efficacy following a high-fidelity simulation experience. The study had two phases: Phase One established the content validity of the surveys through expert review and think-aloud sessions with students. Phase Two administered the surveys online to 58 students to evaluate their internal consistency and reliability. Results showed the surveys had good internal consistency and the Reflective Thinking Instrument was found to be reliable. Further development of the surveys is recommended to fully establish their validity and make them viable for broader use.
Keynote Data Matters JISC What is the impact? Six years of learning analytics...Bart Rienties
The Open University (OU) was an early adopter of learning analytics, and after six years has had the opportunity to reflect on the impact of large scale adoption across the institution.
Has there been an impact on student retention/progress/completion?
How are the positives (or negatives) reflected in student satisfaction surveys?
What worked, what didn't, and with this benefit of hindsight what is, or should be, next?
Metaphor of Thought on Online Teaching during Lockdown by Medical and Dental ...ijtsrd
Online learning has become the mainstay during this COVID 19 lockdown. Students in the professional courses had to adjust themselves to the new teaching method. The present study has been conducted to evaluate and compare the metaphor of thought by the medical and dental students regarding online teaching. A self directed questionnaire was given to 200 participants 120 medical, 80 dental by Google form. Students were between 17 23 years age, pursuing their first year. Questions were given under five subheadings with three options'yes’, 'somewhat’ and 'no’. The responses were analyzed. 47.9 medical and 31.6 dental students replied 'yes’ for blended learning. 40 medical and 30.9 dental students opined there was no contentment with the subject. Mentors advice was useful for 44.4 medical and 59.9 dental students. Only 6.8 medical and 19.1 dental students could be able to manage time.26.8 medical and 13.4 dental students were satisfied with the clarity on the subject. Mixed responses were given by medical and dental students. Medical students preferred blended learning than dental students. Mentors advice was more helpful for dental students. Most of the medical students could manage time when compared to dental students. This study represents the opinion of medical and dental students for online learning. Dr. R. Ravi Sunder | Dr. I. Jyothi Padmaja | Dr. Neelima. P "Metaphor of Thought on Online Teaching during Lockdown by Medical and Dental Students- A Comparative Study" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38640.pdf Paper Url: https://www.ijtsrd.com/medicine/physiology/38640/metaphor-of-thought-on-online-teaching-during-lockdown-by-medical-and-dental-students-a-comparative-study/dr-r-ravi-sunder
Show me the data! Actionable insight from open coursesMartin Hawksey
This document discusses using analytics and data to gain actionable insights from open online courses. It outlines how data can be collected from various sources like YouTube, Google Analytics, and Canvas APIs to analyze student engagement patterns using metrics like social networks, discourse, and content. The goal is to detect subpopulations and understand factors like isolation, group dynamics, and information brokers to improve the learning experience.
What have we learned from 6 years of implementing learning analytics amongst ...Bart Rienties
By Professor Bart Rienties, Head of Academic Professional Development, Institute of Educational Technology, The Open University, UK
Abstract
The Open University UK (OU) has been implementing learning analytics since 2014, starting with one or two modules to its current practice of large-scale implementation across all its 400+ modules and 170.000+ students and 4000+ teaching staff. While a range of reviews (e.g., Adenij, 2019) and scholarly repositories (e.g., Web of Science) indicate that the OU is the largest contributor to academic output in learning analytics in the world, behind the flashy publications and practitioner outputs there are a range of complex issues in terms of ethics and privacy, data infrastructures, buy-in from staff, student engagement, and how to make sense of big data in a complex organisation like the OU.
Based upon large-scale big data research we found some interesting tensions in both design and educational theory, such as:
– 69% of engagement by students on a week by week basis is determined by how teachers are designing courses (i.e., learning design and instructional design indeed directly influence behaviour and cognition), but many teachers seem reluctant to change their learning design based upon data of what works and what does not work (e.g., making sense of data, agency);
– How teachers engage with predictive learning analytics (PLA) significantly improves student outcomes, but only a minority of teachers actually use PLA;
– Some disadvantaged groups engage more actively in OU courses, but nonetheless perform lower than non-disadvantaged students.
During this CELDA keynote I would like to share some of my own reflections of how the OU has implemented learning analytics, and how these insights are helping towards a stronger evidence-base for data-informed change. Furthermore, by sharing some of the lessons learned from implementing learning analytics on a large scale I hope to provide some dos and don’ts in terms of how you might consider to use data in your own practice and context.
Technology across the Care Continuum Paper.docxwrite5
The document discusses using technology effectively across the continuum of care. It provides instructions for an assessment where students will describe technology use across the care continuum in a selected healthcare system. They must include: 1) an overview of the care continuum in their system, 2) how technologies are used and types of technologies/communication systems, 3) strengths/weaknesses and how to manage change/technology to improve outcomes, and 4) tie it to current nursing/informatics theories. Students must also write a 1-page executive summary capturing the current state, proposed improvements, risks of changes, and recommended next steps.
This document summarizes a journal article that proposes an e-Learning Maturity Model (eMM) as a framework to help institutions assess and improve their e-learning capabilities. The eMM is designed to assess an institution's ability to develop, deploy, and support e-learning. It draws from similar maturity models used in software engineering to benchmark processes. Implementing an eMM could provide institutions a roadmap to guide improvements and allow them to benchmark their e-learning capabilities against other institutions.
Mitigating errors of representation: a practical case study of the University...Sonia Whiteley
The document discusses efforts to mitigate errors in the University Experience Survey (UES), a survey of undergraduate students in Australian universities. To address errors of representation, the 2013 and 2014 implementations of the UES used administrative data to generate a national sampling frame and clearly defined the target population. Response rates were increased through incentives, reminders, and monitoring representation. Item-level non-response was reduced through input controls in the online questionnaire. The modifications aimed to minimize coverage errors, increase survey response and representation, and reduce missing data.
This document summarizes research on the workforce outcomes of On-the-Job Training (OJT) funded by the Workforce Investment Act (WIA) in Ohio. The study used Ohio's Longitudinal Data Archive to compare outcomes of 1,115 individuals who received WIA-funded OJT between 2006-2008 to a propensity score matched group of 27,160 non-OJT participants. The analysis found that OJT participants had an average 11 percentage point higher employment rate and $1,100 higher average quarterly wages in the 4 years after participation compared to the non-OJT group. The results provide evidence that WIA-funded OJT improves long-term workforce outcomes for trainees in Ohio.
Natural Language Processing (NLP), RAG and its applications .pptxfkyes25
1. In the realm of Natural Language Processing (NLP), knowledge-intensive tasks such as question answering, fact verification, and open-domain dialogue generation require the integration of vast and up-to-date information. Traditional neural models, though powerful, struggle with encoding all necessary knowledge within their parameters, leading to limitations in generalization and scalability. The paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" introduces RAG (Retrieval-Augmented Generation), a novel framework that synergizes retrieval mechanisms with generative models, enhancing performance by dynamically incorporating external knowledge during inference.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
11. OSCE 創新學習與雲端大數據高峰論壇
PISA 國際學生能力評量計劃
PISA (the Programme for International Student Assessment)
PISA 2012 共有 65 個國家或地區參與,超過 51 萬名15 歲學生進行兩小時的紙
筆式評量。
閱讀、數學和科學三大素養
PISA 2012 results in focus: What 15-year-olds know and what they can do with what
they know. (2014). Retrieved from www.oecd.org/pisa
20. OSCE 創新學習與雲端大數據高峰論壇
Awaisu, A., Abd Rahman, N. S., Mohamed, M. H. N., Bux, S., & Nazar, N. I. M. (2010). Malaysian pharmacy students' assessment
of an objective structured clinical examination (OSCE). American Journal of Pharmaceutical Education, 74(2), 9.
the majority of the
respondents remained neutral
on many questions, thereby
limiting the generalizabilty of
the results
大多數藥學生 沒意見
21. OSCE 創新學習與雲端大數據高峰論壇
Problems with the OSCE rating system
OSCE的評分問題
OSCE depends on the raters’ impression of the performance of the students.
Even if the raters are well trained using a standardized method of assessment, there is
no format for presenting the raters’ assessments.
Kubota, Y., Yano, Y., Seki, S., Takada, K., Sakuma, M., Morimoto, T., . . . Hiraide, A. (2011). Assessment of pharmacy students' communication competence using the
roter interaction analysis system during objective structured clinical examinations. American Journal of Pharmaceutical Education, 75(3), 6.
22. OSCE 創新學習與雲端大數據高峰論壇
Malaysian pharmacy students‘ assessment of OSCE
馬來西亞藥學生對OSCE意見
A 13-station OSCE included: patient counseling and communication, clinical
pharmacokinetics (CPK), identification and resolution of drug-related problems (DRPs),
and literature evaluation/drug information provision.
A majority felt the tasks required in some stations required a higher degree of
learning than they had achieved.
Future efforts should include providing clearer instructions at OSCE stations and
balancing the complexity of the competencies assessed.
Awaisu, A., Abd Rahman, N. S., Mohamed, M. H. N., Bux, S., & Nazar, N. I. M. (2010). Malaysian pharmacy students' assessment
of an objective structured clinical examination (OSCE). American Journal of Pharmaceutical Education, 74(2), 9.
23. OSCE 創新學習與雲端大數據高峰論壇
OSCEs are costly, requiring significant
time, teaching staff, and space
OSCE很花錢 花時間 花人力 花空間
(Hussainy, Styles, & Duncan, 2012)
Hussainy, S. Y., Styles, K., & Duncan, G. (2012). A virtual practice environment to develop communication skills in
pharmacy students. American Journal of Pharmaceutical Education, 76(10), 8. doi:10.5688/ajpe7610202
24. OSCE 創新學習與雲端大數據高峰論壇
OSCE implementation: High-end and Low-end costs estimation.
OSCE的花費
The high-end costs were €145.23 per student (= NT $5000)
the low-end costs were €31.51 per student (= NT $1100)
Palese, A., Bulfone, G., Venturato, E., Urli, N., Bulfone, T., Zanini, A., . . . Dante, A. (2012). The
cost of the objective structured clinical examination on an Italian nursing bachelor's degree
course. Nurse Education Today, 32(4), 422-426. doi:10.1016/j.nedt.2011.03.003
25. OSCE 創新學習與雲端大數據高峰論壇
Strategies for reducing costs
如何減少花費
Creating a network of nursing bachelor's degree courses for swapping scenarios,
cases and clinical situations
Cooperating with Patient Associations to involve them as volunteers in the clinical
scenarios
Establishing links with hospitals to obtain space for conducting the OSCE
Palese, A., Bulfone, G., Venturato, E., Urli, N., Bulfone, T., Zanini, A., . . . Dante, A. (2012). The
cost of the objective structured clinical examination on an Italian nursing bachelor's degree
course. Nurse Education Today, 32(4), 422-426. doi:10.1016/j.nedt.2011.03.003
33. OSCE 創新學習與雲端大數據高峰論壇
Physical attractiveness on academic performance
外表對成績影響
Photographs of the student ID
Approval from the institution
Multiple raters (1-5)
The same 50 images for normalization
Google 大仁科大正妹
French, M. T., Robins, P. K., Homer, J. F., & Tapsell, L. M. (2009). Effects of physical
attractiveness, personality, and grooming on academic performance in high school. Labour
Economics, 16(4), 373-382.
34. OSCE 創新學習與雲端大數據高峰論壇
Grooming hasthe largest effect on GPAformale students,having a
very attractive personality is most importantfor female students.
乾淨對男生成績比較重要,魅力對女生成績比較重要
Personal appearance measures
Models
Males (N = 2487) Females (N = 2878)
A B C D E F G H
Very physically attractive 0.055 − 0.122* 0.080** − 0.047
Below average physical attractiveness − 0.146** − 0.005 − 0.114 − 0.044
Very attractive personality 0.145*** 0.081 0.173*** 0.145***
Below average personality attractiveness − 0.182** − 0.114 − 0.128* − 0.091
Very well groomed 0.263*** 0.274*** 0.163*** 0.114**
Below average grooming − 0.492*** − 0.468*** − 0.197* − 0.155
R squared 0.219 0.222 0.235 0.237 0.29 0.294 0.293 0.297
linear regression results for the effects of personal appearance on overall GPA
French, M. T., Robins, P. K., Homer, J. F., & Tapsell, L. M. (2009). Effects of physical attractiveness, personality, and grooming on
academic performance in high school. Labour Economics, 16(4), 373-382. doi:10.1016/j.labeco.2009.01.001
35. OSCE 創新學習與雲端大數據高峰論壇
More
Attractive female students earn higher grades than unattractive ones (Hernández-
Julián & Peters, 2015)
Rating of charisma and the display of an attractive photograph were both positively
associated with Teaching effectiveness ratings (Rannelli et al., 2014)
Scholars‘ physical appearance is significantly correlated with their research
performance (Dilger, Lutkenhoner, & Muller, 2015)
Lombarts, Kmjmh. (2014). A (good) look at the rating of teaching effectiveness: Towards holistic and programmatic assessment. Medical Education, 48(8), 744-747. doi:10.1111/medu.12491
Rannelli, L., Coderre, S., Paget, M., Woloschuk, W., Wright, B., & McLaughlin, K. (2014). How do medical students form impressions of the effectiveness of classroom teachers? Medical
Education, 48(8), 831-837. doi:10.1111/medu.12420
Hernández-Julián, Rey, & Peters, Christina. (2015). Student appearance and academic performance. Metropolitan State University of Denver.
36. OSCE 創新學習與雲端大數據高峰論壇
But
Attractiveness can be a disadvantage in some situations (Lombarts, 2014)
Disadvantaged when applying for military
Less may be expected of them
Appearance is significantly smaller for both male and female students in online course
environments (Hernández-Julián & Peters, 2015)
Scholars' research performance is especially correlated with perceived
trustworthiness (Dilger, Lutkenhoner, & Muller, 2015)
37. OSCE 創新學習與雲端大數據高峰論壇
Reliability and validity of OSCE
OSCE的信效度議題
Interrater reliability
Internal consistency
Test–retest reliability
Marking tools
Pass mark
Concurrent and predictive validity
Validity as ‘Real World’ assessment
Rushforth, H. E. (2007). Objective structured clinical examination (OSCE): Review of literature and implications for nursing
education. Nurse Education Today, 27(5), 481-490. doi:10.1016/j.nedt.2006.08.009
38. OSCE 創新學習與雲端大數據高峰論壇
Limitations of OSCEs
OSCE的限制
Since 2 parallel OSCEs were conducted, the training emphasized consistency in
grading between each pair of examiners to ensure the same scores were achieved by
a student.
However, no data were generated to support the sufficiency of this in ensuring the
validity and reliability of the examination.
Overall scores on the OSCE are often not very reliable
Awaisu, A., Abd Rahman, N. S., Mohamed, M. H. N., Bux, S., & Nazar, N. I. M. (2010). Malaysian pharmacy students' assessment
of an objective structured clinical examination (OSCE). American Journal of Pharmaceutical Education, 74(2), 9.
39. OSCE 創新學習與雲端大數據高峰論壇
It is more difficult to reliably assess
communication skills than clinical skills.
溝通能力比診斷能力難評量
Brannick, M. T., Erol-Korkmaz, H. T., & Prewett, M. (2011). A systematic review of the reliability of objective structured clinical
examination scores. Medical Education, 45(12), 1181-1189. doi:10.1111/j.1365-2923.2011.04075.x
40. OSCE 創新學習與雲端大數據高峰論壇
Assessment of pharmacy students’
communication competence
評量藥學生的溝通能力
The patient case used for the OSCE was that of a diabetic patient.
Interview to gather information regarding the patient’s medical history.
Each interview was limited to 5 minutes.
The 3 raters who evaluated the students, who were certified as official raters by the
Common Achievement Test Organization in Japan.
The raters used a 6-point global rating scale (6=the student’s performance did not
differ from that of a real pharmacist.)
Kubota, Y., Yano, Y., Seki, S., Takada, K., Sakuma, M., Morimoto, T., . . . Hiraide, A. (2011). Assessment of pharmacy students' communication competence using the
roter interaction analysis system during objective structured clinical examinations. American Journal of Pharmaceutical Education, 75(3), 6.
41. OSCE 創新學習與雲端大數據高峰論壇
Roter Interaction Analysis System (RIAS): A method for coding medical dialogue
Kubota, Y., Yano, Y., Seki, S., Takada, K., Sakuma, M., Morimoto, T., . . . Hiraide, A. (2011). Assessment of pharmacy students' communication competence using the
roter interaction analysis system during objective structured clinical examinations. American Journal of Pharmaceutical Education, 75(3), 6.
42. OSCE 創新學習與雲端大數據高峰論壇
Figure 2. Relationship between the students’ global rating
score and number of utterances in the business category
(R = 0.4305; P = 0.1092)
Figure 1. Relationship between the students’ global rating sc
and number of utterances in the socio-emotional category
(R= 0.662; P < 0.01).
A good interview in the OSCE has been defined as one consisting of a numerous
variety of utterances, especially in the socio-emotional category.
跟社會情感有關的對話越多,OSCE成績越高.
Kubota, Y., Yano, Y., Seki, S., Takada, K., Sakuma, M., Morimoto, T., . . . Hiraide, A. (2011). Assessment of pharmacy students' communication competence using the
roter interaction analysis system during objective structured clinical examinations. American Journal of Pharmaceutical Education, 75(3), 6.
46. OSCE 創新學習與雲端大數據高峰論壇
Robots that can adapt like animals
機器人像動物一樣適應
Cully, Antoine, Clune, Jeff, Tarapore, Danesh, & Mouret, Jean-Baptiste. (2015). Robots that can adapt like animals. Nature, 521(7553), 503-U476. doi:10.1038/nature14422
Using the Intelligent Trial
and Error algorithm, robots,
like animals, can quickly
adapt to recover from
damage.
56. OSCE 創新學習與雲端大數據高峰論壇
模擬藥局
Students had to imagine patients’ characteristics and
could not observe important aspects, such as body language, which help a pharmacist
appropriately communicate with a patient.
Hussainy, S. Y., Styles, K., & Duncan, G. (2012). A virtual practice environment to develop communication skills in
pharmacy students. American Journal of Pharmaceutical Education, 76(10), 8. doi:10.5688/ajpe7610202
57. OSCE 創新學習與雲端大數據高峰論壇
A virtual practice environment
Figure 2. Example of how a case was presented to students in the Communication and Counseling Tutorials
Hussainy, S. Y., Styles, K., & Duncan, G. (2012). A virtual practice environment to develop communication skills in
pharmacy students. American Journal of Pharmaceutical Education, 76(10), 8. doi:10.5688/ajpe7610202
58. OSCE 創新學習與雲端大數據高峰論壇
Scenario-based simulation course training on
nurses‘ communication competence
以模擬課程訓練護生溝通技巧
Compare the effect of a traditional course versus scenario-based simulation training
on
nurses' communication competency,
communication self-efficacy, and
communication performance in discharge planning OSCE.
Hsu, L. L., Chang, W. H., & Hsieh, S. I. (2015). The effects of scenario-based simulation course training on nurses' communication competence and
self-efficacy: A randomized controlled trial. Journal of Professional Nursing, 31(1), 37-49. doi:10.1016/j.profnurs.2014.05.007
59. OSCE 創新學習與雲端大數據高峰論壇 Table 2. Study Protocol for the Two Groups
Hsu, L. L., Chang, W. H., & Hsieh, S. I. (2015). The effects of scenario-based simulation course training on nurses' communication competence and
self-efficacy: A randomized controlled trial. Journal of Professional Nursing, 31(1), 37-49. doi:10.1016/j.profnurs.2014.05.007
60. OSCE 創新學習與雲端大數據高峰論壇
Hsu, L. L., Chang, W. H., & Hsieh, S. I. (2015). The effects of scenario-based simulation course training on nurses' communication competence and
self-efficacy: A randomized controlled trial. Journal of Professional Nursing, 31(1), 37-49. doi:10.1016/j.profnurs.2014.05.007
61. OSCE 創新學習與雲端大數據高峰論壇
Hsu, L. L., Chang, W. H., & Hsieh, S. I. (2015). The effects of scenario-based simulation course training on nurses' communication competence and
self-efficacy: A randomized controlled trial. Journal of Professional Nursing, 31(1), 37-49. doi:10.1016/j.profnurs.2014.05.007
Table 5. Between-Subjects Effects of the Communication Training Course on the Communication Competence
and Self-Efficacy at the First Posttest (N = 116)
Table 6. Between-Subjects Effects of the Communication Training Course on the Learning Satisfaction and Communication
Performance at the First or Second Posttest (N = 116 at T2 and n = 78 at T3)
62. OSCE 創新學習與雲端大數據高峰論壇
Empathy is related to OSCE scores
同理心跟OSCE分數有關
Ogle, J., Bushnell, J. A., & Caputi, P. (2013). Empathy is related to clinical competence in medical care. Medical Education,
47(8), 824-831. doi:10.1111/medu.12232
1 - - 2 - - 3 - - 4 - - 5 - - 6 - - 7
not at all a lot
(1) Did the physician provide the opportunity for the patient to give his/her opinion?
(2) Did the physician treat the patient as an equal partner?
(3) Did the physician show understanding of the patient’s point of view?
(4) Did the physician try to put him/herself in the position of the patient?
(5) Did the physician show interest in the patient’s opinion?
(6) Did the physician put the patient under pressure?
(7) Did the physician "preach"?
(8) Did the physician admonish the patient?
(9) Was the physician responsive to the patient?
66. OSCE 創新學習與雲端大數據高峰論壇
Emotion recognition with the facial
expression software
用表情辨識軟體辨識情緒
Students’ emotional states from the recorded clips were recognized by using
FaceReader facial expression software
(http://www.noldus.com/facereader/facereader-online).
The method for indicating whether the emotional status of the subject is positive or
negative is based on a valence value.
The valence indicates whether the facial expressions are positive or negative
67. OSCE 創新學習與雲端大數據高峰論壇
Emotional states of the clips
Figure 4. The valences of an example clip indicating the generally negative emotion of a student
N
Valence
Mean SD 95% CI to mean
24 -0.07 0.14 -0.13 to -0.01
Table 7. Valence mean scores, standard deviations
70. OSCE 創新學習與雲端大數據高峰論壇
Reliable differences in sensor features
characterizing low vs. high creativity students
高低創造力成績的差異The low creativity students had
– marginally fewer total fixations
– significantly shorter total saccade path length
– significantly lower average saccade speed
– marginally lower saccade speed standard deviation
– marginally more short term excitement
– marginally greater standard deviation of frustration
Classifier TPR FPR F-measure
Rule Nnge 85.7 21.4 85.4
Naive bayes – simple 76.2 33.3 75.7
True Positive Rate, higher is better.
False Positive Rate, lower is better.
77. OSCE 創新學習與雲端大數據高峰論壇
The GAP, silver, and unsolved times significantly relate to the
PMP and the physics posttest scores for low performers
遊戲測毅力對低成就比較有用
PMP GAP Unsolved Silver Gold Enjoy Self-p
GAP .51**
Unsolved .47** .96**
Silver .42** .81** .62**
Gold .00 .22 .14 .32**
Enjoy .23 .08 .02 .18 .06
Self P .10 −.01 −.01 .00 .03 −.05
Physics post test .30* .33** .31* .29* .08 .18 .15
Correlations for 70 low performers in Newton’s Playground.
GAP = unsolved and silver times; unsolved = unsolved time; silver = silver time; gold = gold time;
enjoy = I enjoyed playing NP; self P = self-report measure of persistence.
78. OSCE 創新學習與雲端大數據高峰論壇
The GAP relates much lower to the PMP for high performers
versus low performers
遊戲測毅力對高成就比較沒用
PMP GAP Unsolved Silver Gold Enjoy Self-p
GAP .22
*
Unsolved .12 .94
**
Silver .31
**
.84
**
.60
**
Gold .12 .30
**
.36
**
.11
Enjoy .10 .11 .11 .09 −.03
Self P .09 −.06 −.05 −.05 −.13 .07
Physics post
test
.16 .02 .01 .04 −.21 .15 .15
Correlations for 84 high performers in Newton’s Playground.
GAP = unsolved and silver times; unsolved = unsolved time; silver = silver time; gold = gold time;
enjoy = I enjoyed playing NP; self P = self-report measure of persistence.
81. OSCE 創新學習與雲端大數據高峰論壇
Upload knowledge to your brain
記憶吐司
穿顱直流電刺激tDCS
Working memory is linked primarily
with brain activity in the
dorsolateral prefrontal cortex
(DLPFC)
Skill acquisition and procedural
learning is linked primarily with
brain activity in primary motor
cortex (M1)
Choe, Jaehoon, Coffman, Brian A, Bergstedt, Dylan T, Ziegler, Matthias, & Phillips, Matthew E. (2016).
Transcranial direct current stimulation modulates neuronal activity and learning in pilot training. Frontiers in
Human Neuroscience, 10, 25. doi:10.3389/fnhum.2016.00034
82. OSCE 創新學習與雲端大數據高峰論壇
Flight path deviation between subjects and autopilot
有刺激比較能控制
Choe, Jaehoon, Coffman, Brian A, Bergstedt, Dylan T, Ziegler, Matthias, & Phillips, Matthew E. (2016). Transcranial direct current stimulation modulates neuronal
activity and learning in pilot training. Frontiers in Human Neuroscience, 10, 25. doi:10.3389/fnhum.2016.00034
84. OSCE 創新學習與雲端大數據高峰論壇
OSCE很有壓力
Majority of the students stated that the OSCE was very stressful (75.5%) and more
than half of them found the exam more stressful than the other exams (51.1%).(Selim,
Ramadan, El-Gueneidy, & Gaafer, 2012)
An OSCE could then be seen as limiting for students who lack verbal skills as well as
for those who are unable to overcome nerves during a stressful event.(Baid, 2011)
OSCE might hinder their performance as a result of the stress of being assessed in a
simulated environment (Hemingway, Stephenson, Roberts, & McCann, 2014)
The OSCE was seen as stressful to students (Bouchoucha, Wikander, & Wilkin, 2013)
85. OSCE 創新學習與雲端大數據高峰論壇
造成壓力的原因
Students' stress and anxiety are related to the new experience with OSCE
Students were stressed by the lack of enough time to deal with the scenario in some
stations.
Selim, A. A., Ramadan, F. H., El-Gueneidy, M. M., & Gaafer, M. M. (2012). Using Objective Structured Clinical Examination (OSCE) in undergraduate
psychiatric nursing education: Is it reliable and valid? Nurse Education Today, 32(3), 283-288. doi:10.1016/j.nedt.2011.04.006
87. OSCE 創新學習與雲端大數據高峰論壇
危機同時也是轉機
An opportunity for students to develop skills in presenting information to others and
managing stressful clinical situations which are essential aspects of intensive care
nursing practice.
Learning to manage anxiety during an OSCE may actually help overall performance of
clinical skills in practice (Byrne & Smyth, 2008)
Baid, H. (2011). The objective structured clinical examination within intensive care nursing
education. Nursing in Critical Care, 16(2), 99-105. doi:10.1111/j.1478-5153.2010.00396.x
88. OSCE 創新學習與雲端大數據高峰論壇
如何設計可提升正向情感的測驗回饋?
Liu, Chia-Ju, Huang, Chin-Fei, Liu, Ming-Chi, Chien, Yu-Cheng, Lai, Chia-Hung, & Huang, Yueh-Min*. (2015).
Does gender influence emotions resulting from positive applause feedback in self-assessment testing?
Evidence from neuroscience. Educational Technology & Society, 18(1), 337-350.
90. OSCE 創新學習與雲端大數據高峰論壇
Two tests
the controlled task the experimental task
30 students (15 males, 15 females; mean age ± S.D. = 19.2 ± 2.0 years) participated in this experiment.
97. OSCE 創新學習與雲端大數據高峰論壇
A correlation of 0.85 was obtained
A comparison of model estimates for the mid-Atlantic region (black) against Disease Control and Prevention (CDC)-
reported influenza-like illness ILI percentages (red), including points over which the model was fit and validated.
Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009). Detecting
influenza epidemics using search engine query data. Nature, 457(7232), 1012-U1014.
98. OSCE 創新學習與雲端大數據高峰論壇
Google Flu Trends 的成功
1. Data 分析能夠創造神奇般準確的結果。
2. 每一個 Data 都能不被遺漏,使得舊有的統計抽樣方法過時。
3. 不用再煩惱 Data 間的因果關係,因為統計的相關性會告訴我們我們想要的資
訊,科學的或是統計的模型不再需要,因為套一句 2008 年在 Wired 發表的論文
《 The End of Theory 》裡的話:「有了足夠的資料,數字會自己說話」。
Big data- are we making a big mistake
http://buzzorange.com/techorange/2014/06/04/big-data-making-big-mistake/