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
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.
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.
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.
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.
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.
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.
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.
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.
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
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.
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
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.
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.
The Structure and Components for the Open Education EcosystemHans Põldoja
Lectio Praecursoria in the doctoral defense, 23 September 2016. Aalto University School of Arts, Design and Architecture. Helsinki, Finland.
The disseration can be downloaded from https://shop.aalto.fi/media/attachments/748b6/Poldoja_verkkoversio.pdf
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.
This presentation has been designed as a starting point for anyone thinking about online learning. It's a very brief overview that looks at some of the outcomes and interactions that might be desired, along with a tool that may be used to help achieve this (with careful learning design). It is not supposed to be exhaustive...more of an indication of potential and something that leads to more questions.
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
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.
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
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.
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.
The Structure and Components for the Open Education EcosystemHans Põldoja
Lectio Praecursoria in the doctoral defense, 23 September 2016. Aalto University School of Arts, Design and Architecture. Helsinki, Finland.
The disseration can be downloaded from https://shop.aalto.fi/media/attachments/748b6/Poldoja_verkkoversio.pdf
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.
This presentation has been designed as a starting point for anyone thinking about online learning. It's a very brief overview that looks at some of the outcomes and interactions that might be desired, along with a tool that may be used to help achieve this (with careful learning design). It is not supposed to be exhaustive...more of an indication of potential and something that leads to more questions.
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.
This is presentation on library assessment at Pitt University Library System delivered to iSchool Academic Librarianship Graduate students. December 2015.
This poster is presented at Hypertext 2016. The paper available here:
http://halley.exp.sis.pitt.edu/cn3/presentation2.php?taction=system&conferenceID=144&presentationID=8961
This presentation will be presented at the STC 2013 Technical Communication Summit. The purpose is to provide an overview of MOOCs and garner interest in the upcoming STC Tech Comm MOOC.
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
Adaptive Educational Hypermedia: From generation to generationPeter Brusilovsky
Keynote talk slides for Brusilovsky, P. (2004) Adaptive Educational Hypermedia: From generation to generation. In: Proceedings of 4th Hellenic Conference on Information and Communication Technologies in Education, Athens, Greece, September 29 - October 3, 2004, pp. 19-33.
Interoperability - LTI and Experience API (Formerly TinCan) Nine Lanterns
A webinar looking at the differences between SCORM, LTI and the Experience API (TinCan) within a Learning Management System environment. Presented by James Ballard, Senior Analyst at Nine Lanterns. Listen to the presentation: https://attendee.gotowebinar.com/recording/3218434722750502146
Workshop Learner Enhanced Technology: Can activity analytics support understanding engagement a measurable process? Delivered 20th Jan at HETL SOTE 2015, Utah Valley University.
A presentation on Course Design and Implementation of Course Delivery in Open and Distance Learning.
Delivered during University of Ibadan Cascade Training for all Academic Staffs in Distance Learning Programme.
Five D2L Tools to Increase Student Engagement and Instructor Presence D2L Barry
Presentation at Brightspace New Brunswick Connection, May 5, 2017 at University of New Brunswick.
Five D2L Tools to Increase Student Engagement and Instructor Presence – Barry Dahl, D2L
The design solution focused on enhancing the usability of the learning management system, making it easy to use and pleasurable. Students can easily share/access notes, upload assignments and have a discussion at any time.
E/merge Africa Learning Festival Conference 2018
Digital Fluency Workshop - Brenda Mallinson & Shadrack Mbogela
5 modules: Digital Fundamentals; Working with OER; Course Design & Development for online provision; Academic Integrity in a Digital Age; Storage and Access of Digital Resources.
Similar to Adaptive Navigation Support and Open Social Learner Modeling for PAL (20)
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
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.
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.
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.
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.
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.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Adaptive Navigation Support and Open Social Learner Modeling for PAL
1. Adaptive Navigation Support
and Open Social Learner
Modeling for PAL
Peter Brusilovsky
School of Information Sciences,
University of Pittsburgh
2. Key Goals
• Leverage large volume of data left by past
learners to improve learning process
• Better Interface
– Open Social Learner Modeling Interface for visual
learning analytics and content access
• Better Personalization Algorithms
– Enhancing personalized learning algorithms for
personalized guidance and content recommendation
3. Three Directions of Project Work
• Exploring Open Social Learner Modeling interface
– Diverse learning content
– Multiple domains
• Enhancing personalized learning algorithms for
personalized guidance
– Pro-active content recommendation
– Remedial content recommendation and guidance
• Develop architectural support and authoring tools
for Open Social Learner Modeling
– OSLM as a reusable component
– Content and course authoring tools
7. Accessing OSLM ADL Demo
• You can enter our at:
• http://adapt2.sis.pitt.edu/kt
• ADL Usernames:
– adl01, adl02…, adl10
– Passwords are the same as the usernames
• You will see links for courses in Java, SQL and
Python programming
• Note that most adlxx users are "empty", which
means that they do not have activity yet. User adl01
has some activity..
8. OSLM Experience
• Developed full-semester courses with smart
learning content for 3 domains, all accessible
online, used in several universities
– Java Programming
– Introduction to Databases with SQL
– Python Programming
• Evaluated Mastery Grids Interface in many
classroom studies
– Value of open learner model
– Value of social comparison
9. Term / Course Research set up / comments Active
Learner
s
Questio
n
Attempt
s
Annotated
Examples
Viewed
Animated
Examples
Viewed
IS0017 Fall 2013 Mastery Grids and Portal 38 3832 747 -
IS0017 Spring 2014 Social enabled
(preliminary version of MG)
41 2707 551 -
IS0017 Fall 2014 mixed
(random assign toSocial and non-social groups)
65 4563 1936 670
IS0017 Spring 2015 mixed
(random assign toSocial and non-social groups)
36 2146 947 281
IS0017 Fall 2015 mixed
(random assign toSocial and non-social groups)
58 7109 2689 1165
ASU Fall 2014 Social features enabled Recommendations
enabled
100 9285 4186 -
ASU Fall 2015 Different recommendation algorithms (2
groups)
74 4364 1175 505
CS 401 Fall 2015 mixed
(random assign toSocial and non-social groups)
68 2715 1267 606
WSSU Fall 2013 2 groups, Control / Social 22 876 340 -
WSSU Fall 2014 2 groups, Control / Social 20 1837 618 112
National Sun Yat-Sen
Univ. Taiwan Spring
2015
26 1550 889 420
JAVA Courses
10. Term / Course Research set up / comments Active
Learne
rs
Questio
n
Attemp
ts
Annotated
Examples
Viewed
IS 1022 Fall 2013 Mastery Grids and Portal 15 530 212
IS 1022 Fall 2014 Social features enabled 33 1194 793
IS 1022 Spring 2015 Social features enabled 18 224 277
IS 1022 Fall 2015 Social features enabled 32 526 787
IS 2710 Fall 2013 Mastery Grids and Portal 44 510 213
IS 2710 Fall 2014 2 groups, Control / Social 97 6819 2876
IS 2710 Spring 2015 Social features enabled 33 3616 1506
IS 2710 Fall 2015 2 groups, Control / Social 56 3486 1531
SQL Courses
11. Term / Course Research set up / comments Active
Learne
rs
Questio
n
Attemp
ts
Annotated
Examples
Viewed
Animated
Examples
Viewed
Parso
ns
AALTO Universty
Fall 2015
Social and non-social groups 490 7909 6187 4545 9158
IS0012 Fall 2015 Social features enabled 19 1301 586 548 1628
Python Courses
12. OSLM – Some Findings
• OLM/OSLM significantly improve learning
engagement, problem performance, learning
gain
• Social comparison in OSLM further increase
learner engagement
• OSLM helps students to work more efficiently
• OSLM preserves mastery orientation
13. OSLM Increases Engagement
Variable
OSM OSLM
U
Mean Mean
Sessions 3.93 6.26 685.500*
Topics coverage 19.0% 56.4% 567.500**
Total attempts to problems 25.86 97.62 548.500**
Correct attempts to problems 14.62 60.28 548.000**
Distinct problems attempted 7.71 23.51 549.000**
Distinct problems attempted correctly 7.52 23.11 545.000**
Distinct examples viewed 18.19 38.55 611.500**
Views to example lines 91.60 209.40 609.000**
MG loads 5.05 9.83 618.500**
MG clicks on topic cells 24.17 61.36 638.500**
MG click on content cells 46.17 119.19 577.500**
MG difficulty feedback answers 6.83 14.68 599.500**
Total time in the system 5145.34 9276.58 667.000**
Time in problems 911.86 2727.38 582.000**
Time in MG (navigation) 2260.10 4085.31 625.000**
14. Aggregate: The Architecture behind MG
• Extension of our original architecture ADAPT2
• Allows transparent connection of independent
smart learning content that is interactively
delivered by smart content servers
• Supports extensive tracking of learner activities,
learner record storage, learner modeling, group
modeling, social comparison
• Supports multi-domain course authoring,
content brokering and concept brokering
16. Smart Content and Content Authoring
• We created large volume of reusable smart content
– With activity tracing, content brokering, authoring
• Pittsburgh team smart content
– Interactive examples: Java, SQL, Python
– Java exercises
– SQL problems
– Python exercises
– YouTube video sections
• Helsinki team smart content
– Animated examples (Java and Python)
– Parsons problems (Python)
17. Full Support for Instructors
• Create course for any domain as sequence of any
topics
• Connect smart learning content of several kinds
from multiple content servers
• Create own content if existing content is not
sufficient
• Create groups and subgroups, assign to classes
• Observe class/group work with MG
18. Open Content, Open Source
• All developed content could be reused right from our
content server or by installing own content server
• All the sources are available in GitHub
– The Mastery Grids Interface, back-end Aggregate and documentation
can be found here.
– User model services can be found in here.
– QuizJET Interface, Authoring Tool, Content Brokering and
documentations can be found here.
– QuizPET Interface, Authoring Tool, Content Brokering and
documentations can be found here.
– Parson Problem Authoring Tool can be found here.
– Annotated Examples Interface, Authoring Tool, Content Brokering and
documentations can be found here.
– Animated Examples Authoring Tool can be found here.
– Videos User Interface, Authoring Tool, Content Brokering and
documentations can be found here
19. Algorithms
• Student Modeling
– Several data-driven student modeling approaches
– Most notable is FAST, an extension of BKT that can
use additional data New work: multi-content social
student modeling
• Recommendation
– Several algorithms for proactive content
recommendation and remedial example
recommendation
– Performed several studies demonstrating the value of
recommendation
20. Further Information
• Project Home page
– Explanations, demos, videos, flyers
– http://adapt2.sis.pitt.edu/wiki/ or http://bit.ly/1Ty5KOr
• GitHub
– Sources, installation, system documentation
– https://github.com/PAWSLabUniversityOfPittsburgh
– https://github.com/acos-server/
• Publications, conference presentations
– Interface, algorithms, studies, evaluation data
– Available from the project home page