Empirical studies of adaptive annotation in the educational context have demonstrated that it can help students to acquire knowledge faster, improve learning outcomes, reduce navigational overhead, and encourage non-sequential navigation. Over the last 8 years we have explored a lesser known effect of adaptive annotation – its ability to significantly increase student engagement in working with non-mandatory educational content. In the presence of adaptive link annotation, students tend to access significantly more learning content; they stay with it longer, return to it more often and explore a wider variety of learning resources. This talk will present an overview of our exploration of the addictive links effect in many course-long studies, which we ran in several domains (C, SQL and Java programming), for several types of learning content (quizzes, problems, interactive examples). The first part of the talk will review our exploration of a more traditional knowledge-based personalization approach and the second part will focus on more recent studies of social navigation and open social student modeling
Course-Adaptive Content Recommender for Course AuthoringPeter Brusilovsky
Developing online courses is a complex and time-consuming
process that involves organizing a course into a sequence of topics and
allocating the appropriate learning content within each topic. This task
is especially difficult in complex domains like programming, due to the
incremental nature of programming knowledge, where new topics extensively
build upon domain concepts that were introduced in earlier lessons.
In this paper, we propose a course-adaptive content-based recommender
system that assists course authors and instructors in selecting the most
relevant learning material for each course topic. The recommender system
adapts to the deep prerequisite structure of the course as envisioned
by a specific instructor, while unobtrusively deducing that structure from
problem-solving examples that the instructor uses to present course concepts.
We assessed the quality of recommendations and examined several
aspects of the recommendation process by using three datasets collected
from two different courses.While the presented recommender system was
built for the domain of introductory programming, our course-adaptive
recommendation approach could be used in a variety of other domains.
Personalized Online Practice Systems for Learning ProgrammingPeter Brusilovsky
Computer programming is quickly transitioning from being just a key competency in computer and information science majors to being a desired skill for students in a wide range of fields. Yet, it is also one of the most challenging subjects to learn. While learning by doing is a critical component in mastering programming skills, neither the traditional educational process nor standard learning support tools provide sufficient opportunities for programming practice. In this talk, I will present our research on personalized programming practice systems for Java, Python, and SQL, which attempt to bridge this known gap in learning programming. A programming practice system engages students in practicing programming skills beyond a relatively small number of graded assignments and exams. To support learning by doing, an online practice system offers a range of interactive “smart content” such as program animations, worked examples, and various kinds of programming problems with an automatic assessment. The main challenges for online practice systems are to motivate students to practice and to guide them to the most appropriate smart content given their course goals and knowledge levels. In this talk, I will review a range of AI technologies, such as student modeling, navigation support, social comparison, and content recommendation, which support efficient programming practice. I will also discuss how personalized practice system could support COVID-19-influenced switch to online learning while maintaining an extensive level of feedback expected from an efficient learning process.
22 January 2018 HEFCE open event “Using data to increase learning gains and t...Bart Rienties
With the Teaching Excellence Framework being implemented across England, a lot of higher education institutions have started to ask questions about what it means to be “excellent” in teaching. In particular, with the rich and complex data that all educational institutions gather that could potentially capture learning gains, what do we actually know about our students’ learning journeys? What kinds of data could be used to infer whether our students are actually making affective (e.g., motivation), behavioural (e.g., engagement), and/or cognitive learning gains? Please join us on 22 January 2018 in lovely Milton Keynes at a free OU- and HEFCE-supported event on Using data to increase learning gains and teaching excellence.
14.00-15.00 Measuring learning gains with (psychometric) questionnaires
Dr Sonia Ilie, Prof Jan Vermunt, Prof Anna Vignoles (University of Cambridge, UK): Learning gain: from concept to measurement
Dr Fabio Arico (University of East Anglia): Learning Gain and Confidence Gain Through Peer-instruction: the role of pedagogical design
Dr Paul Mcdermott & Dr Robert Jenkins (University of East Anglia): A Methodology that Makes Self-Assessment an Implicit Part of the Answering Process
15.00-15.45 Measuring employability learning gains
Dr Heike Behle (University of Warwick): Measuring employability gain in Higher Education. A case study using R2 Strengths
Fiona Cobb, Dr Bob Gilworth, David Winter (University of London): Careers Registration Learning Gain project
Using Learning Analytics to Assess Innovation & Improve Student Achievement John Whitmer, Ed.D.
Presentation about Learning Analytics for JISC network event; discussion of research findings and implications for individual and institutions considering a Learning Analytics project. Also discuss implications for my work with Blackboard on "Platform Analytics."
Course-Adaptive Content Recommender for Course AuthoringPeter Brusilovsky
Developing online courses is a complex and time-consuming
process that involves organizing a course into a sequence of topics and
allocating the appropriate learning content within each topic. This task
is especially difficult in complex domains like programming, due to the
incremental nature of programming knowledge, where new topics extensively
build upon domain concepts that were introduced in earlier lessons.
In this paper, we propose a course-adaptive content-based recommender
system that assists course authors and instructors in selecting the most
relevant learning material for each course topic. The recommender system
adapts to the deep prerequisite structure of the course as envisioned
by a specific instructor, while unobtrusively deducing that structure from
problem-solving examples that the instructor uses to present course concepts.
We assessed the quality of recommendations and examined several
aspects of the recommendation process by using three datasets collected
from two different courses.While the presented recommender system was
built for the domain of introductory programming, our course-adaptive
recommendation approach could be used in a variety of other domains.
Personalized Online Practice Systems for Learning ProgrammingPeter Brusilovsky
Computer programming is quickly transitioning from being just a key competency in computer and information science majors to being a desired skill for students in a wide range of fields. Yet, it is also one of the most challenging subjects to learn. While learning by doing is a critical component in mastering programming skills, neither the traditional educational process nor standard learning support tools provide sufficient opportunities for programming practice. In this talk, I will present our research on personalized programming practice systems for Java, Python, and SQL, which attempt to bridge this known gap in learning programming. A programming practice system engages students in practicing programming skills beyond a relatively small number of graded assignments and exams. To support learning by doing, an online practice system offers a range of interactive “smart content” such as program animations, worked examples, and various kinds of programming problems with an automatic assessment. The main challenges for online practice systems are to motivate students to practice and to guide them to the most appropriate smart content given their course goals and knowledge levels. In this talk, I will review a range of AI technologies, such as student modeling, navigation support, social comparison, and content recommendation, which support efficient programming practice. I will also discuss how personalized practice system could support COVID-19-influenced switch to online learning while maintaining an extensive level of feedback expected from an efficient learning process.
22 January 2018 HEFCE open event “Using data to increase learning gains and t...Bart Rienties
With the Teaching Excellence Framework being implemented across England, a lot of higher education institutions have started to ask questions about what it means to be “excellent” in teaching. In particular, with the rich and complex data that all educational institutions gather that could potentially capture learning gains, what do we actually know about our students’ learning journeys? What kinds of data could be used to infer whether our students are actually making affective (e.g., motivation), behavioural (e.g., engagement), and/or cognitive learning gains? Please join us on 22 January 2018 in lovely Milton Keynes at a free OU- and HEFCE-supported event on Using data to increase learning gains and teaching excellence.
14.00-15.00 Measuring learning gains with (psychometric) questionnaires
Dr Sonia Ilie, Prof Jan Vermunt, Prof Anna Vignoles (University of Cambridge, UK): Learning gain: from concept to measurement
Dr Fabio Arico (University of East Anglia): Learning Gain and Confidence Gain Through Peer-instruction: the role of pedagogical design
Dr Paul Mcdermott & Dr Robert Jenkins (University of East Anglia): A Methodology that Makes Self-Assessment an Implicit Part of the Answering Process
15.00-15.45 Measuring employability learning gains
Dr Heike Behle (University of Warwick): Measuring employability gain in Higher Education. A case study using R2 Strengths
Fiona Cobb, Dr Bob Gilworth, David Winter (University of London): Careers Registration Learning Gain project
Using Learning Analytics to Assess Innovation & Improve Student Achievement John Whitmer, Ed.D.
Presentation about Learning Analytics for JISC network event; discussion of research findings and implications for individual and institutions considering a Learning Analytics project. Also discuss implications for my work with Blackboard on "Platform Analytics."
Using Learning Analytics to Create our 'Preferred Future'John Whitmer, Ed.D.
One certainty about the future of higher education is that online technologies will play an increasingly central role in the creation and delivery of learning experiences, whether through mobile apps, MOOCs, open content, ePortfolios, and other resources. As adoption increases, the ‘digital exhaust’ recording technology use has increasing potential to understand student learning. The emergent field of Learning Analytics analyzes this data to provide actionable insights for students, for faculty, and for administrators. What have we learned in Learning Analytics to date? What challenges remain? How should we apply Learning Analytics to create our ‘preferred’ future’ that supports deep and meaningful learning
What data from 3 million learners can tell us about effective course designJohn Whitmer, Ed.D.
Presentation of research findings and implications from a large-scale analysis of LMS activity and grade data from across 927 institutions, 70,000 courses, and 3.3 million students. This webinar will speak to the promise (and potential pitfalls) of large-scale learning analytics research to promote student success.
The Achievement Gap in Online Courses through a Learning Analytics LensJohn Whitmer, Ed.D.
Presentation at San Diego State University on April 12, 2013.
Educational researchers have found that students from under-represented minority families and other disadvantaged demographic backgrounds have lower achievement in online (or hybrid) courses compared to face-to-face course sections (Slate, Manuel, & Brinson Jr, 2002; Xu & Jaggars, 2013). However, these studies assume that "online course" is a homogeneous entity, and that student participation is uniform. The content and activity of the course is an opaque "black box", which leads to conclusions that are speculative at best and quite possibly further marginalize the very populations they intend to advocate for.
The emerging field of Learning Analytics promises to break open this black box understand how students use online course materials and the relationship between this use and student achievement. In this presentation, we will explore the countours of Learning Analytics, look at current applications of analytics, and discuss research applying a Learning Analytics research method to students from at-risk backgrounds. The findings of this research challenge stereotypes of these students as technologically unsophisticated and identify concrete learning activities that can support their success.
The Virtuous Loop of Learning Analytics & Academic Technology Innovation John Whitmer, Ed.D.
Faculty and academic departments creating innovative educational practices are often starved for useful data and analysis to determine whether their innovations made a difference. Research has found that this data is a systematically significant predictor of success much more powerful than traditional demographic or academic preparedness variables. This leads to a “virtuous loop” in which digital technology adoption enables assessment which then improves educational practices using those technologies.
This presentation was delivered at the Online Learning Consortium Collaborate Event, November 19, 2015.
Presentation by Rebecca Ferguson at Learning and Knowledge 2015 (LAK15), Poughkeepsie, NY, USA.
Massive open online courses (MOOCs) are now being used across the world to provide millions of learners with access to education. Many learners complete these courses successfully, or to their own satisfaction, but the high numbers who do not finish remain a subject of concern for platform providers and educators. In 2013, a team from Stanford University analysed engagement patterns on three MOOCs run on the Coursera platform. They found four distinct patterns of engagement that emerged from MOOCs based on videos and assessments. However, not all platforms take this approach to learning design. Courses on the FutureLearn platform are underpinned by a social-constructivist pedagogy, which includes discussion as an important element. In this paper, we analyse engagement patterns on four FutureLearn MOOCs and find that only two clusters identified previously apply in this case. Instead, we see seven distinct patterns of engagement: Samplers, Strong Starters, Returners, Mid-way Dropouts, Nearly There, Late Completers and Keen Completers. This suggests that patterns of engagement in these massive learning environments are influenced by decisions about pedagogy. We also make some observations about approaches to clustering in this context.
2017 UK/IE MoodleMoot: What makes a good moodle quiz? Lessons from the Open U...Tim Hunt
A talk about two things in tandem: good practices for using the Moodle quiz; and how the quiz is used in reality at the Open University. Hopefully those two things have some things in common.
Moving through MOOCs: Pedagogy, Learning and Patterns of EngagementRebecca Ferguson
Presentation for ECTEL 2015, Toledo, Spain (the detailed version).
The related, shorter, presentation is at http://www.slideshare.net/dougclow/moving-through-moocs
2019 Midwest Scholarship of Teaching & Learning (SOTL) conference presentation. The goal of this presentation is to share our data-informed approach to re-engineer the exam design, delivery, grading, and item analysis process in order to construct better exams that maximize all students potential to flourish. Can we make the use of exam analytics so easy and time efficient that faculty clearly see the benefit? For more info see our blog at https://kaneb.nd.edu/real/
Improving Student Achievement with New Approaches to DataJohn Whitmer, Ed.D.
Presentation delivered at WASC ARC conference on April 11, 2013 on the CSU Data Dashboard and Chico State Learning Analytics case study.
Chico State Case Study: Academic technologies collect highly detailed student usage data. How can this data be used to understand and predict student performance, especially of at-risk students? This presentation will discuss research on a high-enrollment undergraduate course exploring the relationship between LMS activity, student background characteristics, current enrollment information, and student achievement.
CSU Data Dashboard: By monitoring on-track indicators institutional leaders can better understand not only which milestones students are failing to reach, but why they are not reaching them. It can also help campuses to design interventions or policy changes to increase student success and to gauge the impact of interventions.
Teaching in MOOCs: Unbundling the roles of the educatorRebecca Ferguson
Teaching in MOOCs: Unbundling the roles of the educator, a presentation given at the design4learning conference at The Open University, Milton Keynes, UK by Rebecca Ferguson (co-authored with Denise Whitelock) on 26 November 2014.
Fostering students’ engagement and learning through UNEDTrivial: a gamified s...UNED
UNEDTrivial is an activity plugin of Moodle that allows teachers to create spaced quizzes based on two principles in the field of educational psychology
• Testing effect: according to which the best method to fix the knowledge is to answer questions after study sessions.
• Spacing education: the spaced repetition of the same items, at specific intervals, increases in long-term retention.
Participants enrolled in a UNEDTrivial receive daily through email reminders of the questions they must answer. Through the feedback provided in each response attempt, students build their knowledge, correcting failures and reinforcing the successes. Wrong answer questions are sending again in the delay established by the instructor to check the knowledge acquisition and increase the long-term retention.
UNEDTrivial offers their complete analytics page where teachers can track the progress of their students. Furthermore, UNEDTrivial uses gamification to increase student engagement such as a leaderboard where students can track their progress, in addition to competing with other classmates. Moreover, UNEDTrivial is compatible with Moodle badges, so it's possible to assign it when a student has closed all questions in a UNEDtrivial.
UNEDTrivial y available at Moodle official plug-in repository:
https://moodle.org/plugins/mod_unedtrivial
Classsourcing: Crowd-Based Validation of Question-Answer Learning Objects @ I...Jakub Šimko
A simple approach for assessing answer validity information from a student crowd in an online learning scenario context. Raises the questions about using of the student crowds for enhancing learning content and online student collaboration.
Personalized Learning: Expanding the Social Impact of AIPeter Brusilovsky
Slide of my keynote talk at SIAIA '23 workshop held at AAAI 2023:
The use of AI in Education could be traced to the early days of AI. While the publicity associated with the most recent wave of AI applications rarely mentions education, it is through the improvement in education AI could achieve an impressive social impact. In particular, the AI ability to personalize the learning process could make a large difference in a context where learners' knowledge could be radically different from learner to learner. Modern computer and internet technologies can now bring the power of learning in the forms of MOOCs, online textbooks, and zoom courses truly worldwide. Yet, without personalization, the potential of these technologies is not fully leveraged. In this talk, I will review several generations of research on personalized learning and discuss tools, technologies, and infrastructures for personalized learning that we are currently exploring.
Using Learning Analytics to Create our 'Preferred Future'John Whitmer, Ed.D.
One certainty about the future of higher education is that online technologies will play an increasingly central role in the creation and delivery of learning experiences, whether through mobile apps, MOOCs, open content, ePortfolios, and other resources. As adoption increases, the ‘digital exhaust’ recording technology use has increasing potential to understand student learning. The emergent field of Learning Analytics analyzes this data to provide actionable insights for students, for faculty, and for administrators. What have we learned in Learning Analytics to date? What challenges remain? How should we apply Learning Analytics to create our ‘preferred’ future’ that supports deep and meaningful learning
What data from 3 million learners can tell us about effective course designJohn Whitmer, Ed.D.
Presentation of research findings and implications from a large-scale analysis of LMS activity and grade data from across 927 institutions, 70,000 courses, and 3.3 million students. This webinar will speak to the promise (and potential pitfalls) of large-scale learning analytics research to promote student success.
The Achievement Gap in Online Courses through a Learning Analytics LensJohn Whitmer, Ed.D.
Presentation at San Diego State University on April 12, 2013.
Educational researchers have found that students from under-represented minority families and other disadvantaged demographic backgrounds have lower achievement in online (or hybrid) courses compared to face-to-face course sections (Slate, Manuel, & Brinson Jr, 2002; Xu & Jaggars, 2013). However, these studies assume that "online course" is a homogeneous entity, and that student participation is uniform. The content and activity of the course is an opaque "black box", which leads to conclusions that are speculative at best and quite possibly further marginalize the very populations they intend to advocate for.
The emerging field of Learning Analytics promises to break open this black box understand how students use online course materials and the relationship between this use and student achievement. In this presentation, we will explore the countours of Learning Analytics, look at current applications of analytics, and discuss research applying a Learning Analytics research method to students from at-risk backgrounds. The findings of this research challenge stereotypes of these students as technologically unsophisticated and identify concrete learning activities that can support their success.
The Virtuous Loop of Learning Analytics & Academic Technology Innovation John Whitmer, Ed.D.
Faculty and academic departments creating innovative educational practices are often starved for useful data and analysis to determine whether their innovations made a difference. Research has found that this data is a systematically significant predictor of success much more powerful than traditional demographic or academic preparedness variables. This leads to a “virtuous loop” in which digital technology adoption enables assessment which then improves educational practices using those technologies.
This presentation was delivered at the Online Learning Consortium Collaborate Event, November 19, 2015.
Presentation by Rebecca Ferguson at Learning and Knowledge 2015 (LAK15), Poughkeepsie, NY, USA.
Massive open online courses (MOOCs) are now being used across the world to provide millions of learners with access to education. Many learners complete these courses successfully, or to their own satisfaction, but the high numbers who do not finish remain a subject of concern for platform providers and educators. In 2013, a team from Stanford University analysed engagement patterns on three MOOCs run on the Coursera platform. They found four distinct patterns of engagement that emerged from MOOCs based on videos and assessments. However, not all platforms take this approach to learning design. Courses on the FutureLearn platform are underpinned by a social-constructivist pedagogy, which includes discussion as an important element. In this paper, we analyse engagement patterns on four FutureLearn MOOCs and find that only two clusters identified previously apply in this case. Instead, we see seven distinct patterns of engagement: Samplers, Strong Starters, Returners, Mid-way Dropouts, Nearly There, Late Completers and Keen Completers. This suggests that patterns of engagement in these massive learning environments are influenced by decisions about pedagogy. We also make some observations about approaches to clustering in this context.
2017 UK/IE MoodleMoot: What makes a good moodle quiz? Lessons from the Open U...Tim Hunt
A talk about two things in tandem: good practices for using the Moodle quiz; and how the quiz is used in reality at the Open University. Hopefully those two things have some things in common.
Moving through MOOCs: Pedagogy, Learning and Patterns of EngagementRebecca Ferguson
Presentation for ECTEL 2015, Toledo, Spain (the detailed version).
The related, shorter, presentation is at http://www.slideshare.net/dougclow/moving-through-moocs
2019 Midwest Scholarship of Teaching & Learning (SOTL) conference presentation. The goal of this presentation is to share our data-informed approach to re-engineer the exam design, delivery, grading, and item analysis process in order to construct better exams that maximize all students potential to flourish. Can we make the use of exam analytics so easy and time efficient that faculty clearly see the benefit? For more info see our blog at https://kaneb.nd.edu/real/
Improving Student Achievement with New Approaches to DataJohn Whitmer, Ed.D.
Presentation delivered at WASC ARC conference on April 11, 2013 on the CSU Data Dashboard and Chico State Learning Analytics case study.
Chico State Case Study: Academic technologies collect highly detailed student usage data. How can this data be used to understand and predict student performance, especially of at-risk students? This presentation will discuss research on a high-enrollment undergraduate course exploring the relationship between LMS activity, student background characteristics, current enrollment information, and student achievement.
CSU Data Dashboard: By monitoring on-track indicators institutional leaders can better understand not only which milestones students are failing to reach, but why they are not reaching them. It can also help campuses to design interventions or policy changes to increase student success and to gauge the impact of interventions.
Teaching in MOOCs: Unbundling the roles of the educatorRebecca Ferguson
Teaching in MOOCs: Unbundling the roles of the educator, a presentation given at the design4learning conference at The Open University, Milton Keynes, UK by Rebecca Ferguson (co-authored with Denise Whitelock) on 26 November 2014.
Fostering students’ engagement and learning through UNEDTrivial: a gamified s...UNED
UNEDTrivial is an activity plugin of Moodle that allows teachers to create spaced quizzes based on two principles in the field of educational psychology
• Testing effect: according to which the best method to fix the knowledge is to answer questions after study sessions.
• Spacing education: the spaced repetition of the same items, at specific intervals, increases in long-term retention.
Participants enrolled in a UNEDTrivial receive daily through email reminders of the questions they must answer. Through the feedback provided in each response attempt, students build their knowledge, correcting failures and reinforcing the successes. Wrong answer questions are sending again in the delay established by the instructor to check the knowledge acquisition and increase the long-term retention.
UNEDTrivial offers their complete analytics page where teachers can track the progress of their students. Furthermore, UNEDTrivial uses gamification to increase student engagement such as a leaderboard where students can track their progress, in addition to competing with other classmates. Moreover, UNEDTrivial is compatible with Moodle badges, so it's possible to assign it when a student has closed all questions in a UNEDtrivial.
UNEDTrivial y available at Moodle official plug-in repository:
https://moodle.org/plugins/mod_unedtrivial
Classsourcing: Crowd-Based Validation of Question-Answer Learning Objects @ I...Jakub Šimko
A simple approach for assessing answer validity information from a student crowd in an online learning scenario context. Raises the questions about using of the student crowds for enhancing learning content and online student collaboration.
Personalized Learning: Expanding the Social Impact of AIPeter Brusilovsky
Slide of my keynote talk at SIAIA '23 workshop held at AAAI 2023:
The use of AI in Education could be traced to the early days of AI. While the publicity associated with the most recent wave of AI applications rarely mentions education, it is through the improvement in education AI could achieve an impressive social impact. In particular, the AI ability to personalize the learning process could make a large difference in a context where learners' knowledge could be radically different from learner to learner. Modern computer and internet technologies can now bring the power of learning in the forms of MOOCs, online textbooks, and zoom courses truly worldwide. Yet, without personalization, the potential of these technologies is not fully leveraged. In this talk, I will review several generations of research on personalized learning and discuss tools, technologies, and infrastructures for personalized learning that we are currently exploring.
Data-Driven Education: Using Big Educational Data to Improve Teaching and Learning. Keynote slides for 15th International Conference on Web-Based Learning, ICWL 2016, Rome, Italy, October 26–29.
The Value of Social: Comparing Open Student Modeling and Open Social Student ...Peter Brusilovsky
Brusilovsky, P., Somyurek, S., Guerra, J., Hosseini, R., and Zadorozhny, V. (2015) The Value of Social: Comparing Open Student Modeling and Open Social Student Modeling. In: F. Ricci, K. Bontcheva, O. Conlan and S. Lawless (eds.) Proceedings of 23nd Conference on User Modeling, Adaptation and Personalization (UMAP 2015), Dublin, Ireland, , June 29 - July 3, 2015, Springer Verlag, pp. 44-55, also available at http://link.springer.com/chapter/10.1007/978-3-319-20267-9_4.
Mastery Grids: An Open Source Social Educational Progress VisualizationPeter Brusilovsky
Presentation for EC-TEL 2015 paper:
Loboda, T., Guerra, J., Hosseini, R., and Brusilovsky, P. (2014) Mastery Grids: An Open Source Social Educational Progress Visualization. In: S. de Freitas, C. Rensing, P. J. Muñoz Merino and T. Ley (eds.) Proceedings of 9th European Conference on Technology Enhanced Learning (EC-TEL 2014), Graz, Austria, September 16-19, 2014, pp. 235-248.
Interactions of reading and assessment activitiesSergey Sosnovsky
Reading and assessment are elementary activities for knowl- edge acquisition in online learning. Assessments represented as quizzes can help learners to identify gaps in their knowledge and understanding, which they can then overcome by reading the corresponding text-based course material. Reversely, quizzes can be used to evaluate reading com- prehension. The predominantly self-regulated interaction of reading and quiz activities in learning systems used in higher education has been little studied. In this paper, we examine this interaction using scroll and log data from an online undergraduate course (N=142). By analyzing pro- cesses and sequential patterns in user sessions, we identified six session clusters for characteristic reading and quiz patterns potentially relevant for adaptive learning support. These clusters showed that individual user sessions included either mainly reading or quizzes, but rarely both.
VII Jornadas eMadrid "Education in exponential times". Erkan Er: "Predicting ...eMadrid network
VII Jornadas eMadrid "Education in exponential times". Erkan Er: "Predicting Peer-Review Participation at Large Scale Using an Ensemble Learning Method". 04/07/2017.
IUI2017 SmartLearn keynote: Intelligent Interfaces for Open Social Student M...Peter Brusilovsky
In this talk I will introduce the emerging technology of
Open Social Student Modeling (OSSM) and review several
projects performed in our research lab to investigate the
potential of OSSM.
OSSM is a recent extension of Open Student Modeling
(OSM), a popular technology in the area of personalized
learning systems. While in traditional personalized systems,
student models were hidden “under the hood” and used to
personalize the educational process; open student modeling
introduced the ability to view and modify the state of
students’ own knowledge to support reflection, selforganized
learning, and system transparency. Open Social
Student Modeling takes this idea one step further by
allowing students to explore each other’s models or an
aggregated model of the class. The idea to make OSM
social was originally suggested and explored by Bull [1; 2].
Over the last few years, our team explored several
approaches to present OSSM in a highly visual form and
evaluated these approaches in a sequence of classroom and
lab studies. I will present a summary of this work
introducing such systems as QuizMap [3], Progressor [4],
and Mastery Grids [5] and reviewing most interesting
research evidence collected by the studies.
NC3ADL Session: Leveraging Digital Media to Personalize the Path to College Readiness (Presentation provided by Angie Smajstrla)
This session will share examples of how educators are leveraging adaptable, affordable online resources from the non-profit NROC project to support teaching and learning innovations. We will look especially at how Developmental Math - An Open Program is being used both in and out of the classroom to personalize learning experiences for students striving to accelerate the path to college readiness. NROC resources are available to all NC Community Colleges through a partnership with NCCCS.
Presenter(s): Angie Smajstrla (The NROC Project); Wanda Barker (NCCCS); Kathy Davis (NCCCS); Jonathon Sweetin (NCCCS)
Program code examples (known also as worked examples) play a crucial role in learning how to program. Instructors use examples extensively to demonstrate the semantics of the programming language being taught and to highlight the fundamental coding patterns. Programming textbooks allocate considerable space to present and explain code examples. To make the process of studying code examples more interactive, CS education researchers developed a range of tools to engage students in the study of code examples. These tools include codecasts (codemotion,codecast,elicasts), interactive example explorers (WebEx, PCEX), and tutoring systems (DeepTutor). An important component in all types of worked examples is code explanations associated with specific code lines or code chunks of an example. The explanations connect examples with general programming knowledge explaining the role and function of code fragments or their behavior. In textbooks, these explanations are usually presented as comments in the code or as explanations on the margins. The example explorer tools allow students to examine these explanations interactively. Tutoring systems, which engage students in explaining the code, use these model explanations to check student responses and provide scaffolding. In all these cases, to make a worked example re-usable beyond its presentation in a lecture, the explanations have to be authored by instructors or domain experts i.e., produced and integrated into a specific system. As the experience of the last 10 years demonstrated, these explanations are hard to obtain. Those already collected are usually “locked” in a specific example-focused system and can’t be reused. The purpose of this working group is to support broader re-used of worked examples augmented with explanations. Our current plan is to develop а standard approach to represent explained examples. This approach will enable an example created for any of the existing systems to be explored in a standard format and imported into any other example-focused system. We plan to follow a successful experience of the PEML working group focused on re-using programming exercises.
SANN: Programming Code Representation Using Attention Neural Network with Opt...Peter Brusilovsky
Slides of CIKM 2023 paper by Muntasir Hoq, Sushanth Reddy Chilla, Melika Ahmadi Ranjbar, Peter Brusilovsky and Bita Akram
https://dl.acm.org/doi/10.1145/3583780.3615047
Action Sequence Mining and Behavior Pattern Analysis for User ModelingPeter Brusilovsky
Slides of my talk at 2022 Workshop on Temporal Aspects of User Modeling
Tracing learner interaction with educational content has recently emerged as a centerpiece of learning analytics. Augmented by various data mining technologies, learner data has been used to predict learner success and failure, prevent dropouts, and inform university officials about student progress. While the majority of existing learning analytics approaches ignore the time aspect in the learning data, recent research indicated that not just what the learners do, but how and in which order they do it is critical to understand differences between learners, model their behavior, and predict their performance. In my talk, I will focus on the application of action sequence mining as a tool to extract temporal patterns of learning behavior and recognize cohorts of learners with divergent behavior. I will review three case studies of using sequence mining with learner data, present the obtained results, and discuss their importance for user modeling and personalization.
Tutorial at UMAP 2022:
In recent years, the use of Artificial Intelligence (AI) technologies expanded to many areas where they directly affect the lives of many
people. AI-based approaches advise human decision-makers who should be released on bail, whether it is a good time to discharge a
patient from a hospital and whether a specific student is at risk to fail a course. Such an extensive use in AI in decision making came with
a range of protentional problems that have been extensively studied over the last few years. Recognition of these problems motivated a
rapid rise of research on “human-centered AI”, which attempted to address and minimize the negative effects of using AI technologies.
Among the ideas of human-centered AI is user control - engaging users in affecting AI decision making to prevent possible errors and
biases. In my talk, I will focus on the application of user control in one popular area of AI application, adaptive information access.
Adaptive information access systems such as personalized search and recommender systems attempt to model their users to help them in
finding the most relevant information. Yet, user modeling and personalization mechanisms might not always work as expected resulting
in errors, biases, and suboptimal behavior. Combining the decision power or AI with the ability of the user to guide and control it brings
together the strong sides of artificial and human intelligence and could lead to better results. This tutorial will provide a systematic review
of approaches focused on adding various kinds of user control to adaptive information access systems and discuss lessons learned,
prospects, and challenges of this direction of research.
Human-Centered AI in AI-ED - Keynote at AAAI 2022 AI for Education workshopPeter Brusilovsky
Abstract: In recent years, the use of Artificial Intelligence (AI) technologies expanded to many areas directly affecting the lives of millions. AI-based approaches advise human decision-makers who should be released on bail, whether it is a good time to discharge a patient from a hospital and whether a specific student is at risk to fail a course. Such extensive use in AI in decision making came with a range of protentional problems that have been extensively studied over the last few years. Recognition of these problems motivated a rapid rise of research on “human-centered AI”, which attempted to address and minimize the negative effects of using AI technologies. The majority of work on human-centered AI focus on various types of Human-AI collaboration through such technologies as transparency, explainability, and user control. In my talk, I will review how the ideas of Human-AI collaboration, transparency, explainability, and user control have been used in educational applications of AI in the past and will discuss now new ideas in this research area developed outside of AI-Ed could be creatively applied in educational context.
User Control in AIED (Artificial Intelligence in Education)Peter Brusilovsky
Slides of my intro to "Meet the Expert" session at AIED 2021. This is a subset of slides of a longer presentation on user control in AI extended with many specific examples from AIED area.
The Return of Intelligent Textbooks - ITS 2021 keynote talkPeter Brusilovsky
Early research on hypermedia learning and Web-based education featured a strong stream of work on intelligent and adaptive textbooks, which combined the knowledge modeling ideas from the field of intelligent tutoring with rich linking offered by the hypermedia and the Web. However, over the next ten years from 2005 to 2015 this area was relatively quiet as the focus of research in e-learning has shifted to other topics and other creative ideas to leverage the power of Internet. A recent gradual shift of the whole publication industry from printed books to electronic books followed by a rapid growth or the volume of online books re-ignited interests to “more intelligent” textbooks. The research on the new generation of intelligent textbooks engaged a larger set of technologies and engaged scholars from a broader range of areas including machine learning, natural language understanding, social computing, etc. In my talk I will review the past and present of research on intelligent textbooks from its origins to the diverse modern work providing examples of most interesting technologies and research results.
Data-Driven Education 2020: Using Big Educational Data to Improve Teaching an...Peter Brusilovsky
Modern educational settings from regular classrooms to MOOCs produce a a rapidly increasing volume of data that captures individual learning progress of millions of students at different level of granularity. This presence of this data opens a unique opportunity to re-engineer traditional education and build and develop a range of efficient data-driven approaches to support teaching and learning. In my talk, I will present several ways to use big educational data explored in our lab. The focus will be on open social learning modeling and identifying individual differences through sequential pattern mining, but several other approaches will be mentioned. Open social learning modeling and sequential pattern mining provides two considerably different examples on using educational data. One offers an immediate use of class interaction history to develop more engaging content access while another shows how big data could be used to uncover important individual differences that could be used to optimize the process for individual leaners.
Two Brains are Better than One: User Control in Adaptive Information AccessPeter Brusilovsky
In recent years, the use of Artificial Intelligence (AI) technologies expanded to many areas where they directly affect the lives of many people. AI-based approaches advise human decision-makers who should be released on bail, whether it is a good time to discharge a patient from a hospital and whether a specific student is at risk to fail a course. Such an extensive use in AI in decision making came with a range of protentional problems that have been extensively studied over the last few years. Recognition of these problems motivated a rapid rise of research on “human-centered AI”, which attempted to address and minimize the negative effects of using AI technologies. Among the ideas of human-centered AI is user control - engaging users in affecting AI decision making to prevent possible errors and biases. In my talk, I will focus on the application of user control in one popular area of AI application, adaptive information access. Adaptive information access systems such as personalized search and recommender systems attempt to model their users to help them in finding the most relevant information. Yet, user modeling and personalization mechanisms might not always work as expected resulting in errors, biases, and suboptimal behavior. Combining the decision power or AI with the ability of the user to guide and control it brings together the strong sides of artificial and human intelligence and could lead to better results. In my talk, I review several projects focused on user control in adaptive information access systems and discuss the benefits and challenges of this approach.
UMAP 2019 talk Evaluating Visual Explanations for Similarity-Based Recommenda...Peter Brusilovsky
Tsai, Chun-Hua, and Peter Brusilovsky. 2019. "Evaluating Visual Explanations for Similarity-Based Recommendations: User Perception and Performance." In the 27th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2019, 22-30. Larnaca, Cyprus: ACM.
From Expert-Driven to Data-Driven Adaptive LearningPeter Brusilovsky
Keynote slides for the Workshop on Advancing Education with Data at the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, Aug 14, 2017
Stereotype Modeling for Problem-Solving Performance Predictions in MOOCs and ...Peter Brusilovsky
Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, UMAP2017, pp 76-84
Stereotypes are frequently used in real life to classify students according to their performance in class. In literature, we can find many references to weaker students, fast learners, struggling students, etc. Given the lack of detailed data about students, these or other kinds of stereotypes could be potentially used for user modeling and personalization in the educational context. Recent research in MOOC context demonstrated that data-driven learner stereotypes could work well for detecting and preventing student dropouts. In this paper, we are exploring the application of stereotype-based modeling to a more challenging task -- predicting student problem-solving and learning in two programming courses and two MOOCs. We explore traditional stereotypes based on readily available factors like gender or education level as well as some advanced data-driven approaches to group students based on their problem-solving behavior. Each of the approaches to form student stereotype cohorts is validated by comparing models of student learning: do students in different groups learn differently? In the search for the stereotypes that could be used for adaptation, the paper examines ten approaches. We compare the performance of these approaches and draw conclusions for future research.
Personalization in the Context of Relevance-Based VisualizationPeter Brusilovsky
In this talk, I will review our research attempts to
implement different kinds of personalization in the context
of relevance-based visualization. The goal of this research
stream is to make relevance-based visualization adaptive to
user long-term goals, interests, or prospects rather just
responsive to short term immediate needs such as query
terms. I will present four personalized relevance-based
visualization systems: Adaptive VIBE, TalkExplorer,
SetFusion, and IntersectionExplorer, For each system, I
will present its idea, some evaluation results, and
lessons learned.
https://doi.org/10.1145/3038462.3038474
Adaptive Navigation Support and Open Social Learner Modeling for PALPeter Brusilovsky
This presentation is an overview of Open Social Learner Modeling project. It presents Mastery Grids interface, distributed personalized learning architecture Aggregate, and smart content for Java, Python, and SQL
Slides for invited talk: Brusilovsky, P. (2003) From adaptive hypermedia to the adaptive Web. In: J. Ziegler and G. Szwillus (eds.) Interaktion in Bewegung. (Proceedings of Mensch & Computer 2003, Stuttgart, September 7-10, 2003) Stuttgart, Germany: B. G. Teubner, pp. 21-2
Bridging the Digital Gap Brad Spiegel Macon, GA Initiative.pptxBrad Spiegel Macon GA
Brad Spiegel Macon GA’s journey exemplifies the profound impact that one individual can have on their community. Through his unwavering dedication to digital inclusion, he’s not only bridging the gap in Macon but also setting an example for others to follow.
Multi-cluster Kubernetes Networking- Patterns, Projects and GuidelinesSanjeev Rampal
Talk presented at Kubernetes Community Day, New York, May 2024.
Technical summary of Multi-Cluster Kubernetes Networking architectures with focus on 4 key topics.
1) Key patterns for Multi-cluster architectures
2) Architectural comparison of several OSS/ CNCF projects to address these patterns
3) Evolution trends for the APIs of these projects
4) Some design recommendations & guidelines for adopting/ deploying these solutions.
# Internet Security: Safeguarding Your Digital World
In the contemporary digital age, the internet is a cornerstone of our daily lives. It connects us to vast amounts of information, provides platforms for communication, enables commerce, and offers endless entertainment. However, with these conveniences come significant security challenges. Internet security is essential to protect our digital identities, sensitive data, and overall online experience. This comprehensive guide explores the multifaceted world of internet security, providing insights into its importance, common threats, and effective strategies to safeguard your digital world.
## Understanding Internet Security
Internet security encompasses the measures and protocols used to protect information, devices, and networks from unauthorized access, attacks, and damage. It involves a wide range of practices designed to safeguard data confidentiality, integrity, and availability. Effective internet security is crucial for individuals, businesses, and governments alike, as cyber threats continue to evolve in complexity and scale.
### Key Components of Internet Security
1. **Confidentiality**: Ensuring that information is accessible only to those authorized to access it.
2. **Integrity**: Protecting information from being altered or tampered with by unauthorized parties.
3. **Availability**: Ensuring that authorized users have reliable access to information and resources when needed.
## Common Internet Security Threats
Cyber threats are numerous and constantly evolving. Understanding these threats is the first step in protecting against them. Some of the most common internet security threats include:
### Malware
Malware, or malicious software, is designed to harm, exploit, or otherwise compromise a device, network, or service. Common types of malware include:
- **Viruses**: Programs that attach themselves to legitimate software and replicate, spreading to other programs and files.
- **Worms**: Standalone malware that replicates itself to spread to other computers.
- **Trojan Horses**: Malicious software disguised as legitimate software.
- **Ransomware**: Malware that encrypts a user's files and demands a ransom for the decryption key.
- **Spyware**: Software that secretly monitors and collects user information.
### Phishing
Phishing is a social engineering attack that aims to steal sensitive information such as usernames, passwords, and credit card details. Attackers often masquerade as trusted entities in email or other communication channels, tricking victims into providing their information.
### Man-in-the-Middle (MitM) Attacks
MitM attacks occur when an attacker intercepts and potentially alters communication between two parties without their knowledge. This can lead to the unauthorized acquisition of sensitive information.
### Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) Attacks
APNIC Foundation, presented by Ellisha Heppner at the PNG DNS Forum 2024APNIC
Ellisha Heppner, Grant Management Lead, presented an update on APNIC Foundation to the PNG DNS Forum held from 6 to 10 May, 2024 in Port Moresby, Papua New Guinea.
This 7-second Brain Wave Ritual Attracts Money To You.!nirahealhty
Discover the power of a simple 7-second brain wave ritual that can attract wealth and abundance into your life. By tapping into specific brain frequencies, this technique helps you manifest financial success effortlessly. Ready to transform your financial future? Try this powerful ritual and start attracting money today!
1.Wireless Communication System_Wireless communication is a broad term that i...JeyaPerumal1
Wireless communication involves the transmission of information over a distance without the help of wires, cables or any other forms of electrical conductors.
Wireless communication is a broad term that incorporates all procedures and forms of connecting and communicating between two or more devices using a wireless signal through wireless communication technologies and devices.
Features of Wireless Communication
The evolution of wireless technology has brought many advancements with its effective features.
The transmitted distance can be anywhere between a few meters (for example, a television's remote control) and thousands of kilometers (for example, radio communication).
Wireless communication can be used for cellular telephony, wireless access to the internet, wireless home networking, and so on.
test test test test testtest test testtest test testtest test testtest test ...
Addictive links, Keynote talk at WWW 2014 workshop
1. Addictive Links:
Engaging Students through
Adaptive Navigation Support and
Open Social Student Modeling
Peter Brusilovsky with:
Sergey Sosnovsky, Michael Yudelson, Sharon Hsiao
School of Information Sciences,
University of Pittsburgh
4. MOOC Completion Rate
Classic loop user modeling - adaptation in adaptive systems
http://www.katyjordan.com/MOOCproject.html
5. What Else These Students Need?
• Top colleges
– Stanford, CalTech, Princeton, GATech, Penn, Duke..
• Great faculty – top guns in their fields
• Great content
• Top online platform – Coursera
• FREE!
6. The Problem of Engagement
• Great free content and top teachers is not
enough to engage students
• Peter Norvig: Motivation and Engagement are
key problems for MOOCs
• The problem is not new
• A lot of great advanced content
– Works perfectly in lab studies, great gains
– Released to students to enhance learning
– No impact – students do not use it
7. The Case of QuizPACK
• QuizPACK: Quizzes for
Parameterized Assessment of
C Knowledge
• Each question is a pattern of a
simple C program. When it is
delivered to a student the
special parameter is
dynamically instantiated by a
random value within the pre-
assigned borders.
• Used mostly as a self-
assessment tool in two C-
programming courses
8. QuizPACK: Value and Problems
• Good news:
– activity with QuizPACK significantly correlated with
student performance in classroom quizzes
– Knowledge gain rose from 1.94 to 5.37
• But:
– Low success rate - below 40%
– The system is under-used (used less than it deserves)
• Less than 10 sessions at average
• Average Course Coverage below 40%
9. Adding Motivation
• Students need some better motivation to work with non-
mandatory educational content…
• Added classroom quizzes:
– Five randomly initialized questions out of 20-30 questions
assigned each week
• Good results - activity, percentage of active questions,
course coverage - all increased 2-3 times! But still not as
much as we want. Could we do better?
• Maybe students bump into wrong questions? Too easy?
Too complicated? Discouraging…
• Let’s try something that worked in the past adaptive
hypermedia that can guide students to the right content
10. User Model
Collects information
about individual user
Provides
adaptation effect
Adaptive
System
User Modeling side
Adaptation side
User-Adaptive Systems
Classic loop user modeling - adaptation in adaptive systems
11. Adaptive Link Annotation: InterBook
1. Concept role
2. Current concept state
3. Current section state
4. Linked sections state
4
3
2
1
√"
Metadata-based mechanism
12. The Value of ANS
• Lower navigation overhead
– Access the content at the right time
– Find relevant information faster
• Better learning outcomes
– Achieve the same level of knowledge faster
– Better results with fixed time
• Encourages non-sequential navigation
13. Questions of
the current
quiz, served
by QuizPACK
List of annotated
links to all quizzes
available for a
student in the
current course
Refresh
and help
icons
QuizGuide = QuizPACK+ANS
15. QuizGuide: Adaptive Annotations
• Target-arrow abstraction:
– Number of arrows – level of
knowledge for the specific
topic (from 0 to 3).
Individual, event-based
adaptation.
– Color Intensity – learning
goal (current, prerequisite
for current, not-relevant,
not-ready). Group, time-
based adaptation.
n Topic–quiz organization:
16. QuizGuide: Success Rate
n It works!
n One-way ANOVA shows
that mean success value for
QuizGuide is significantly
larger then the one for
QuizPACK:
F(1, 43) = 5.07
(p-value = 0.03).
17. QuizGuide: Motivation
• Adaptive navigation support increased student's
activity and persistence of using the system
Average activity
0
50
100
150
200
250
300
2002 2003 2004
Average num. of
sessions
0
5
10
15
20
2002 2003 2004
Average course
coverage
0%
10%
20%
30%
40%
50%
60%
2002 2003 2004
Active students
0%
20%
40%
60%
80%
100%
2002 2003 2004
n Within the same class QuizGuide session were much longer than
QuizPACK sessions: 24 vs. 14 question attempts at average.
n Average Knowledge Gain for the class rose from 5.1 to 6.5
18. A new value of ANS?
• The scale of the effect is too large…
May be just a good luck?
• New effect after 15 years of research?
• Maybe the effect could only be
discovered in full-scale classroom
studies – while past studies were lab-
based?
19. Round 2: Let’s Try it Again…
• Another study with the same system
– QuizGuide+QuizPACK vs. QuizPACK
• A study with another system using similar kinds
of adaptive navigation support
– NavEx+WebEx vs. WebEx
• NavEx - a value-added ANS front-end for
WebEx - interactive example exploration system
21. Concept-based student modeling
Example 2
Example M
Example 1
Problem 1
Problem 2
Problem K
Concept 1
Concept 2
Concept 3
Concept 4
Concept 5
Concept N
Examples
Problems
Concepts
23. Does it work?
• The increase of the amount of work for the
course
Clicks - Overall
0
50
100
150
200
250
300
Non-adaptive Adaptive
Examples
Quizzes
Lectures - Overall
0
2
4
6
8
10
12
Non-adaptive Adaptive
Examples
Quizzes
Learning Objects - Overall
0
5
10
15
20
25
30
Non-adaptive Adaptive
Examples
Quizzes
24. Is It Really Addictive?
• Are they coming more often? Mostly, but there
is no stable effect
• But when they come, they stay… like with an
addictive game
Clicks - Per Session
0
5
10
15
20
Non-adaptive Adaptive
Examples
Quizzes
Learning Objects - Per
Session
0
1
2
3
4
Non-adaptive Adaptive
Examples
Quizzes
25. Why It Is Working?
• Progress-based annotation
– Displays the progress achieved so far
– Does it work as a reward mechanism?
– Open Student Modeling
• State-based annotation
– Not useful, ready, not ready
– Access activities in the right time
– Appropriate difficulty, keep motivation
27. The Diversity of Work
• C-Ratio: Measures the breadth of exploration
• Goal distance: Measures the depth
Self-motivated Work - C-Ratio
(%)
0
0.2
0.4
0.6
Non-adaptive Adaptive
Quizzes
Examples
Self-motivated Work - Goal
Distance (LO's)
0
5
10
15
20
Non-adaptive Adaptive
Quizzes
Examples
28. Round 3: Trying another domain…
• Is it something relevant to C programming or to
simple kind of content?
• New changes:
– SQL Programming instead of C
– Programming problems (code writing) instead of
questions (code evaluation)
– Comparison of concept-based and topic-based
mechanisms in the same domain and with the same
kind of content
29. • SQL-KnoT delivers online SQL problems, checks student’s
answers and provides a corrective feedback
• Every problem is dynamically generated using a template
and a set of
databases
• All problems have
been assigned to 1
of the course
topics and
indexed with
concepts from the
SQL ontology
SQL Knowledge Tester
30. • To investigate possible influence of concept-based
adaptation in the present of topic-based adaptation we
developed two versions of QuizGuide:
Topic-based Topic-based+Concept-Based
Concept-based vs Topic-based ANS
31. • Two Database Courses (Fall 2007):
§ Undergraduate (36 students)
§ Graduate (38 students)
• Each course divided into two groups:
§ Topic-based navigation
§ Topic-based + Concept-Based Navigation
• All students had access to the same set of SQL-
KnoT problems available in adaptive
(QuizGuide) and in non-adaptive mode (Portal)
Study Design
32. • Total number of attempts made by all students:
in adaptive mode (4081), in non-adaptive mode (1218)
• Students in general were much more willing to access
the adaptive version of the system, explored more
content with it and to stayed with it longer:
Questions
0
25
50
75
100
Quizzes
0
5
10
15
20
25
Topics
0
1
2
3
4
5
6
Sessions
0
1
2
3
4
5 Session Length
0
5
10
15
20
25
Adaptive
Non-adaptive
It works again! Like magic…
33. Round 4: The Issue of Complexity
• Let’s now try it for Java…
• What is the research goal?
• Java is a more sophisticated domain than C
– OOP versus Procedural
– Higher complexity
• Will it work for complex
questions?
• Will it work similarly? 0% 20% 40% 60% 80% 100%
C
Java
language complexity
Easy
Moderate
Hard
37. !! !!
JavaGuide
(Fall 2008)
QuizJET
(Spring 2008)
!! parameters (n=22) (n=31)
Overall User
Statistics
Attempts 125.50 41.71
Success Rate 58.31% 42.63%
Distinct Topics 11.77 4.94
Distinct Questions 46.18 17.23
Average
User Session
Statistics
Attempts 30.34 21.50
Distinct Topics 2.85 2.55
Distinct Questions 11.16 8.88
Magic… Here We Go Again!
38. Round 5: Social Navigation
• Concept-based and topic-based navigation support
work well to increase success and motivation
• Knowledge-based approaches require some
knowledge engineering – concept/topic models,
prerequisites, time schedule
• In our past work we learned that social navigation –
guidance extracted from the work of a community of
learners – might replace knowledge-based guidance
• Social wisdom vs. knowledge engineering
39. Open Social Student Modeling
• Key ideas
– Assume simple topic-based design
– No prerequsites or concept modeling
– Show topic- and content- level knowledge progress of
a student in contrast to the same progress of the class
• Main challenge
– How to design the interface to show student and class
progress over topics?
– We went through several attempts
43. Class vs. Peers
• Peer progress was important, students
frequently accessed content using peer models
• The more the students compared to their peers,
the higher post-quiz scores they received (r=
0.34 p=0.004)
• Parallel IV didn’t allow to recognized good peers
before opening the model
• Progressor added clear peer progress
47. Take-home messages
• A combination of progress-based and state-
based adaptive link annotation increases the
amount and the diversity of student work with
non-mandatory educational content
• The effect is stable and the scale of it is quite
large
• Properly organized Social Navigation might be
at least as successful as the knowledge-based
• Requires a long-term classroom study to
observe
48. Why It Is Important?
• Many systems demonstrated their educational
effectiveness in a lab-like settings: once the students
are pushed to use it - it benefits their learning
• However, once released to real classes, these systems
are under-used - most of them offer additional non-
mandatory learning opportunities
• “Students are only interested in points and grades”
• Convert all tools into credit-bearing activities?
• Or use alternative approaches to increase motivation
49. What we are doing now?
• Exploring new generation of open social
modeling tools in wide variety if classes and
domains from US to Nigeria
– Interested to be a pilot site?
• Exploring more advanced guidance and
modeling approaches based on large volume of
social data
• Applying open social modeling to motivate
readings
50. Acknowledgements
• Joint work with
– Sergey Sosnovsky
– Michael Yudelson
– Sharon Hsiao
• Pitt “Innovation in Education” grant
• NSF Grants
– EHR 0310576
– IIS 0426021
– CAREER 0447083
51. Try It!
• http://adapt2.sis.pitt.edu/kt/
• Brusilovsky, P., Sosnovsky, S., and Yudelson, M. (2009)
Addictive links: The motivational value of adaptive link annotation.
New Review of Hypermedia and Multimedia 15 (1), 97-118.
• Hsiao, I.-H., Sosnovsky, S., and Brusilovsky, P. (2010) Guiding
students to the right questions: adaptive navigation support in an E-
Learning system for Java programming. Journal of Computer Assisted
Learning 26 (4), 270-283.
• Hsiao, I.-H., Bakalov, F., Brusilovsky, P., and König-Ries, B.
(2013) Progressor: social navigation support through open social
student modeling. New Review of Hypermedia and Multimedia [PDF]
Read About It!