This document discusses interfaces for providing transparent and user-controlled recommendations. It outlines problems with single ranked lists and proposes solutions like explaining recommendations, visualizing relevance processes, and allowing users to explore and control personalization. Specific solutions discussed include visualization tools, open learner/user models, and interfaces that combine relevance from multiple sources and allow controlling factors. Studies found that explorable and controllable recommendations better support understanding relevance and finding relevant items.
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.
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.
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.
Iui2015: Personalized Search: Reconsidering the Value of Open User ModelsPeter Brusilovsky
IUI 2015 talk slides: Ahn, J., Brusilovsky, P., and Han, S. (2015) Personalized Search: Reconsidering the Value of Open User Models. In: Proceedings of Proceedings of the 20th International Conference on Intelligent User Interfaces, Atlanta, Georgia, USA, ACM, pp. 202-212
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
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.
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.
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.
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.
Iui2015: Personalized Search: Reconsidering the Value of Open User ModelsPeter Brusilovsky
IUI 2015 talk slides: Ahn, J., Brusilovsky, P., and Han, S. (2015) Personalized Search: Reconsidering the Value of Open User Models. In: Proceedings of Proceedings of the 20th International Conference on Intelligent User Interfaces, Atlanta, Georgia, USA, ACM, pp. 202-212
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
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.
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.
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
This talk presents an overview of approaches for interactive visualization of recommendations in social network streams to facilitate exploratory browsing. It also gives a brief historical background of some of the underlying ideas, of open user modelling and social navigation and social interaction history,.
A joint keynote with Heather O'Brien at the Learning Analytics Summer Institute (LASI) 2019. In here we explore the concept of learner- and user- engagement as relevant for the field of learning analytics.
Tools and Evaluation Techniques to Support Social Awareness in CSCeL: The AVA...EuroCAT CSCL
Tools and Evaluation Techniques to Support Social Awareness in CSCeL: The AVATAR
Niki Lambropoulos and Fintan Culwin presented at the Euro-CAT workshop in Barcelona 05/02/2010
Exploring Tools for Promoting Teacher Efficacy with mLearning (mlearn 2014 Pr...Robert Power
Slides for my presentation with Dean Cristol and Belinda Gimbert of Ohio State University at mLearn 2014, November 4, 2014, at Kadir-Has University in Istanbul, Turkey.
Microcredentialing: Recognizing Student Learning with Digital BadgesStephanie Richter
A college degree is important, but it provides an incomplete picture of a graduate’s knowledge, skills, and experiences. Microcredentialing (awarding recognition for small, granular achievements) may help! By collecting and displaying digital badges online, students can combine evidence from all of their learning activities (including classroom, co-curricular, and outside learning) to promote themselves more effectively. In this session, which was presented at the 2015 SLATE Conference, we discussed what badges are and how to create and award them to your students.
Design a personalized e-learning system based on item response theory and art...eraser Juan José Calderón
Design a personalized e-learning system based on item response theory and artificial neural network approach
Ahmad Baylari, Gh.A. Montazer *
IT Engineering Department, School of Engineering, Tarbiat Modares University, Tehran, Iran
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.
This study was presented in the 20th International Conference on Web Information Systems Engineering (WISE 2019), 19-22 January 2020.
-----
Fumiaki Saito, Yoshiyuki Shoji and Yusuke YamamotoHighlighting Weasel Sentences for Promoting Critical Information Seeking on the WebProceedings of the 20th International Conference on Web Information Systems Engineering (WISE 2019), pp.424-440, Hong Kong, China, November 2019.
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.
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
This talk presents an overview of approaches for interactive visualization of recommendations in social network streams to facilitate exploratory browsing. It also gives a brief historical background of some of the underlying ideas, of open user modelling and social navigation and social interaction history,.
A joint keynote with Heather O'Brien at the Learning Analytics Summer Institute (LASI) 2019. In here we explore the concept of learner- and user- engagement as relevant for the field of learning analytics.
Tools and Evaluation Techniques to Support Social Awareness in CSCeL: The AVA...EuroCAT CSCL
Tools and Evaluation Techniques to Support Social Awareness in CSCeL: The AVATAR
Niki Lambropoulos and Fintan Culwin presented at the Euro-CAT workshop in Barcelona 05/02/2010
Exploring Tools for Promoting Teacher Efficacy with mLearning (mlearn 2014 Pr...Robert Power
Slides for my presentation with Dean Cristol and Belinda Gimbert of Ohio State University at mLearn 2014, November 4, 2014, at Kadir-Has University in Istanbul, Turkey.
Microcredentialing: Recognizing Student Learning with Digital BadgesStephanie Richter
A college degree is important, but it provides an incomplete picture of a graduate’s knowledge, skills, and experiences. Microcredentialing (awarding recognition for small, granular achievements) may help! By collecting and displaying digital badges online, students can combine evidence from all of their learning activities (including classroom, co-curricular, and outside learning) to promote themselves more effectively. In this session, which was presented at the 2015 SLATE Conference, we discussed what badges are and how to create and award them to your students.
Design a personalized e-learning system based on item response theory and art...eraser Juan José Calderón
Design a personalized e-learning system based on item response theory and artificial neural network approach
Ahmad Baylari, Gh.A. Montazer *
IT Engineering Department, School of Engineering, Tarbiat Modares University, Tehran, Iran
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.
This study was presented in the 20th International Conference on Web Information Systems Engineering (WISE 2019), 19-22 January 2020.
-----
Fumiaki Saito, Yoshiyuki Shoji and Yusuke YamamotoHighlighting Weasel Sentences for Promoting Critical Information Seeking on the WebProceedings of the 20th International Conference on Web Information Systems Engineering (WISE 2019), pp.424-440, Hong Kong, China, November 2019.
Scalable Exploration of Relevance Prospects to Support Decision MakingKatrien Verbert
Presented at IntRS 2016 - Interfaces and Human Decision Making for Recommender Systems, workshop at RecSys 2016
Citation: Verbert, K., Seipp, K., He, C., Parra, D., Wongchokprasitti, C., & Brusilovsky, P. (2016). Scalable Exploration of Relevance Prospects to Support Decision Making. Proceedings of the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems co-located with ACM Conference on Recommender Systems (RecSys 2016), Boston, MA, USA, September 16, 2016.
Epistemic networks for Epistemic CommitmentsSimon Knight
The ways in which people seek and process information are fundamentally epistemic in nature. Existing epistemic cognition research has tended towards characterizing this fundamental relationship as cognitive or belief-based in nature. This paper builds on recent calls for a shift towards activity-oriented perspectives on epistemic cognition and proposes a new theory of ‘epistemic commitments’. An additional contribution of this paper comes from an analytic approach to this recast construct of epistemic commitments through the use of Epistemic Network Analysis (ENA) to explore connections between particular modes of epistemic commitment. Illustrative examples are drawn from existing research data on children’s epistemic talk when engaged in collaborative information seeking tasks. A brief description of earlier analysis of this data is given alongside a newly conducted ENA to demonstrate the potential for such an approach.
Paper at: http://oro.open.ac.uk/39254/
To appreciate the paradigm shift involved in the next generation search systems one needs to look back at the traditional approach to resource discovery and compare to the new trends. Here I focus on three aspects:
• Databases versus search engines
• Federated versus integrated search
• Integrated versus modular architecture.
Creating Sustainable Communities in Open Data Resources: The eagle-i and VIVO...Robert H. McDonald
This is the slidedeck for my ACRL 2015 TechConnect Presentation with Nicole Vasilevsky (OHSU). For more on the program see - <a>http://bit.ly/1xcQbCr</a>.
Redesigning the Open Access Institutional RepositoryEdward Luca
This lecture presents a redesign project of UTS's institutional repository, OPUS. It explains some of the challenges faced by libraries in ensuring eRepository participation, and investigates three user groups - academics, librarians, and information seekers. User experience principles are used to address issues around navigation, terminology, and visual identity.
Presented as a guest lecture to Designing for the Web (Spring 2016) students.
4.16.15 Slides, “Enhancing Early Career Researcher Profiles: VIVO & ORCID Int...DuraSpace
Hot Topics: The DuraSpace Community Webinar Series
Series 11: Integrating ORCID Persistent Identifiers with DSpace, Fedora and VIVO
Webinar 3: “Enhancing Early Career Researcher Profiles: VIVO & ORCID Integration”
April 16, 2015
Curated by Josh Brown, ORCID
Presented by: Simeon Warner, Library Information Systems, Cornell University, Jon Corson-Rikert, Head of Information Technology Services, Cornell University and Kristi Holmes, Director, Galter Health Sciences Library, Northwestern University
Scholar Plot –
Scalable Data Visualization Methods for Academic Careers
Kyeongan (Karl) Kwon
PhD Dissertation
Department of Computer Science
University of Houston
Monday July 18, 2016
Abstract
In this dissertation, I have developed scalable data visualization methods to depict a scholar's accomplishments at a glance. The evaluation of scholarly achievements in academia is largely based on the researcher's publication record. This record is communicated in exhaustive detail in the researcher's curriculum vitae (CV) or in summary via her/his h-index. The h-index, although a convenient abstraction, does not consider neither the time of the publication nor the impact factor (IF) of the journal where it appeared. I present a novel method that visually complements the h-index, revealing at a glance the nature of a researcher's scholastic record. This method (which includes the visualizations Scholar Plot and Academic Garden) is particularly appropriate for web interfaces, as it produces information that is compact and simple, yet highly illuminating.
Scholar Plot uses Google Scholar, Impact Factor, and NSF/NIH/NASA funding data to create a temporal representation of a researcher's publication/funding record that blends publication prestige with paper popularity and funding information. Scholar Plot affords an insightful appraisal of academics at one's fingertips. Academic Garden applies to individual academics, departments, colleges, and any other academic group thereof, such as a research lab or a project team. Academic Garden uses the flower metaphor to visually articulate performance of academic entities. The width of the flower's stem is commensurate to the academic funding the entity received (`juice conduit'). The height of the flower's stem is commensurate to the impact of the entity's intellectual products (`visibility'). The diameter of the flower's disc is commensurate to the prestige of the venues where these products appeared (`fancy factor'). Scholar Plot and Academic Garden bring clarity, transparency, and fairness in hiring, promotion, tenure, and funding decisions.
For the validation of the Academic Garden, I ran data analysis using Endowed Chaired Faculty, a prestigious honor in the United States, for the top 10 universities according to the US News Report 2015. The analysis demonstrated that chaired faculty can be predicted using the 3 merit criteria of citations, impact factor, and funding.
Similar to Interfaces for User-Controlled and Transparent Recommendations (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.
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.
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.
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.
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.
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.
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
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.
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.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
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.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
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.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Interfaces for User-Controlled and Transparent Recommendations
1. Interfaces for User-Controlled
and Transparent
Recommendations
Peter Brusilovsky
with Jae-Wook Ahn, Denis Parra, Katrien
Verbert, Chun-Hua Tsai, Jordan Barria-
Pineda
2. Outline
• Fighting the ranked list: problems and solutions
• Transparency and control: two sides of the coin
• Solutions for single-source ranking
– Visualize! Explore! Control!
• Combining relevance sources in Conference Navigator
– TalkExplorer
– Intersection Explorer
– SetFusion
– RelevanceTuner
• Transparency and control for educational recommendation
2
4. • Why an item is at a specific position?
– Items might be relevant for to the user profile (or
query) for different reasons
• Single-source: different parts/aspects of the profile
• Hybrid: different sources of information or approaches
• It might not be the right position!
– A recommendation approach is tuned to an
overall/generic situation, but users could consult
recommendation for different needs
– Some profile aspects, sources, approaches are less
relevant in the current context, but some are more
4
While Single Ranked List is A Problem?
5. What are Possible Solutions?
• Explain (words)
– Why a specific item is considered relevant?
– Why it is placed in a specific position?
• Visualize (beyond ranking list)
– Make the ranking/relevance process transparent
• Explore (change visualization)
– Change visualization parameters to play with the results,
better understand the process, isolate most relevant results
• Control (change how personalization work)
– Change user profile
– Change parameters (how personalization is produced)
5
6. Two Sides of the Same Coin
Explain Visualize
ExploreControl
6
Transparency
Interactivity
No full transparency
without interactivity
Control is challenging
without transparency
10. VIBE (Olsen, 1993)
10
Olsen, K. A., R. R. Korfhage, K. M. Sochats, M. B. Spring, and J. G. Williams. 1993. 'Visualisation of a document
collection: The VIBE system', Information Processing and Management, 29.
13. John O'Donovan, Barry Smyth, Brynjar Gretarsson, Svetlin Bostandjiev, and Tobias Höllerer. 2008. PeerChooser: visual
interactive recommendation. CHI '08
PeerChooser (O’Donovan, 2008)
13
14. Explaining CBR (Tsai, 2019)
Recommending people to meet at the conference using cosine similarity of
users’ publications.
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.
15. Explaining Social Rec (Tsai, 2019)
Recommending people to meet using social connections: degree of
network distance, based on a shared co-authorship network.
Tsai, Chun-Hua, and Peter Brusilovsky. 2019. "Designing Explanation Interfaces for Transparency and Beyond " In
Workshop on Intelligent User Interfaces for Algorithmic Transparency in Emerging Technologies at the 24th ACM
Conference on Intelligent User Interfaces, IUI 2019. Los Angeles, USA.
16. Experiments with Exploration
• Adaptive Vibe (2006-2015)
– With Jae-Wook Ahn
• Relevance Explorer (2013-2016)
– With Katrien Verbert and Denis Parra
• Intersection Explorer (2017-2019)
– With Katrien Verbert, Karsten Seipp, Chen He,
Denis Parra, Bruno Cardoso, Gayane Sedrakyan,
Francisco Gutiérrez
16
17. EXPLORE!
Make the ranking process explorable. Allow users to play with
presentation parameters to understand aspects of relevance and
find best items in the given context
17
19. QuizVIBE (2006)
Ahn, J.-w., Brusilovsky, P., and Sosnovsky, S. (2006) QuizVIBE: Accessing Educational Objects with Adaptive
Relevance-Based Visualization. In: Proc. of World Conference on E-Learning, E-Learn 2006, Honolulu, HI,
USA, October 13-17, 2006, AACE, pp. 2707-2714.
20. CONTROL!
Allow the user to control multiple aspects of the recommendation
process to better adapt personalization for the current context as
well as better explore recommendation results
20
21. What Can Be Controlled?
21
Profile Generation Presentation
Student Model
User Model
Single Source
Fusion
EXPLORE!
22. Open Learner Model (ELM-ART)
22
Weber, G. and Brusilovsky, P. (2001) ELM-ART: An adaptive versatile system for Web-based instruction. International Journal of Artificial
Intelligence in Education 12 (4), 351-384.
23. Open User Model (YourNews)
Ahn, J.-w., Brusilovsky, P., Grady, J., He, D., and Syn, S. Y. (2007) Open user profiles for adaptive news
systems: help or harm? In: 16th international conference on World Wide Web, WWW '07, Banff, Canada, May 8-12,
2007, ACM, pp. 11-20
24. Concept-Level Open User Model
(SciNet)
24
Glowacka, Dorota, Tuukka Ruotsalo, Ksenia Konuyshkova, Kumaripaba Athukorala, Samuel Kaski, and
Giulio Jacucci. 2013. "Directing Exploratory Search: Reinforcement Learning from User Interactions with
Keywords." In international conference on Intelligent user interfaces, IUI '2013, 117-27. Santa Monica, USA:
ACM Press.
25. O'Donovan, John, Barry Smyth, Brynjar Gretarsson, Svetlin Bostandjiev, and Tobias Höllerer. 2008. "PeerChooser: visual
interactive recommendation." In Proceedings of the twenty-sixth annual SIGCHI conference on Human factors in computing
systems, 1085-88. Florence, Italy: ACM.
PeerChooser (Controllable CF)
25
26. TaskSieve: Controlable Personalized Search
Ahn, Jae-wook, Peter Brusilovsky, Daqing He, Jonathan Grady, and Qi Li. 2008. "Personalized Web Exploration with
Task Models." In the 17th international conference on World Wide Web, WWW '08, 1-10. Beijing, China: ACM.
27. TaskSieve Controllable Ranking
• Post-filtering
• Combine query relevance and task relevance
– Alpha * Task_Model_Score + (1-alpha) * Search Score
– Alpha : user control (0.0, 0.5, or 1.0)
• Results
– Better than regular adaptive search
– Better then non adaptive baseline even in cases when
profile was excluded
– Users were really good in deciding when to engage the
profile and how
27
30. VIBE based query-profile fusion
User Profile Terms
Query Terms
Documents
Mixing user profile and query terms as VIBE POI
31. • User profile is added on the same playfield
as user query
• Topology is adaptive
• Mediate between profile (green POI) and
query (red POI) terms
• Browse documents free with control on
profile and query terms
Adaptive topology in VIBE
33. Some Study Results
• A sequence of user studies
– Search vs. VIBE vs. VIBE+NE
• Search -> VIBE -> VIBE+NE offers:
– Better visual separation of relevant documents (system)
– Supports better opening relevant documents (user)
• VIBE+NE supports more meanigful interaction
– No degradation found even with active visual UM
manipulation
– While over performance retained or increased
Ahn, J., Brusilovsky, P., and Han, S. (2015) Personalized Search: Reconsidering the Value of Open User Models. In:
Proceedings of Proceedings of the 20th International Conference on Intelligent User Interfaces, Atlanta, Georgia, USA, March 29-
April 1, 2015, ACM, pp. 202-212
34. TasteWeights: Profile and Mechanism
Control
38
Knijnenburg, Bart P., Svetlin Bostandjiev, John O'Donovan, and Alfred Kobsa. 2012. "Inspectability and Control in Social
Recommenders." In 6th ACM Conference on Recommender System, 43-50. Dublin, Ireland.
36. Talk Relevance in Conference Navigator
• Classic content-based relevance prospects (search)
– Items that has a specific keyword
• Social relevance prospects (browsing)
– Items bookmarked by a specific user
• Tag relevance prospects (browsing)
– Items tagged by a specific tag
• Personal relevance prospects (recommendation)
– Several different recommender engines
– Each engine offer one relevance prospect
40
Brusilovsky, P., Oh, J. S., López, C., Parra, D., and Jeng, W. (2017) Linking information and people in a social
system for academic conferences. New Review of Hypermedia and Multimedia.
41. Relevance Explorer
• Context: multiple dimensions of relevance
– social - users, content - tags, recommender engines
• Using set relevance visualization
– One dimension of relevance = one set
• Agent metaphor to mix user- tag- and
engine-based relevance
– Users, tags, and recommender systems are shown as
agents collecting relevant talks
– Multiple-relevance match -> stronger evidence
46
42. TalkExplorer
• Recommendation engines are shown as agents in parallel to users and tags
• Uses Aduna clustermap library: http://www.aduna-software.com/
47
44. Evaluation
• Setup
– supervised user study
– 21 participants at UMAP 2012 and ACM Hypertext 2012 conferences
• Results
– The more aspects of relevance are fused, the more effective it is for
getting to relevant items. Especially effective are fusions across
relevance dimensions
– The more relevance prospects are merged, the better is the yield, the
easier is to find good items
– Dimensions of relevance are not equal
– ADUNA approach is challenging for beyond fusion of 3 aspects 52
Verbert, K., Parra-Santander, D., and Brusilovsky, P. (2016) Agents Vs. Users: Visual Recommendation of Research Talks
with Multiple Dimension of Relevance. ACM Transactions on Interactive Intelligent Systems 6 (2), Article No. 11
45. Intersection Explorer
• Based on ideas of
Talk Explorer
• New approach for
scalable multi-set
visualization
53
Cardoso, Bruno, Gayane Sedrakyan, Francisco Gutiérrez, Denis Parra, Peter Brusilovsky, and Katrien Verbert. 2019.
'IntersectionExplorer, a multi-perspective approach for exploring recommendations', International Journal of Human-Computer
Studies, 121: 73-92.
46. Intersection Explorer at IUI2017
54
http://halley.exp.sis.pitt.edu/cn3/iestudy3.php?conferenceID=148
47. ScatterViz: Diversity-Focused
Exploration of Hybrid Recommendations
Tsai, Chun-Hua, and Peter Brusilovsky. 2018. "Beyond the Ranked List: User-Driven Exploration and Diversification of Social
Recommendation." In 23rd International Conference on Intelligent User Interfaces, 239--50. Tokyo, Japan: ACM.
48. SetFusion
• Using set relevance visualization in
the familiar Venn diagram form
– One recommendation source = one
set
• Allow controlled ranking
fusion
• Combine ranking with
annotation showing
source(s) of recommendation 57
49.
50. Brief Results of Two Studies
• SetFusion provides strong engaging effect
– Number of engaged users, bookmarked talks,
explored talks doubled
– The effect is larger in UMAP “natural” settings
• SetFusion allows more efficient work
– Increases yield of bookmarks in relation to
overhead actions
• But only 3 dimensions of relevance!
• How to control for more than 3 dimensions?
– See our RelevanceTuner design coming next!
64
51. RelevanceTuner: Control+Visualization
in a Hybrid Social Recommender
Tsai, Chun-Hua and Peter Brusilovsky (2018) Beyond the Ranked List: User-Driven Exploration and Diversification of Social
Recommendation. In 23rd International Conference on Intelligent User Interfaces, 239--50. Tokyo, Japan: ACM.
55. ATEC Workshop
2 0 1 9
Los Angeles
69
Explanations in Mastery Grids
Barria-Pineda,Jordan,andPeterBrusilovsky.2019."Explaining
EducationalRecommendationsThroughaConcept-levelKnowledge
Visualization."InProceedingsofthe24thInternationalConferenceon
IntelligentUserInterfaces:Companion,103--04.NewYork,NY,USA:
ACM.
56. Remedial Recommendations
Textual explanations
# of “struggled” concepts
# of “proficient concepts”
(Knowledge Est. > .66)
70
Barria-Pineda, Jordan, Kamil Akhuseyinoglu, and Peter Brusilovsky. 2019. "Explaining Need-based Educational
Recommendations Using Interactive Open Learner Models." In International Workshop on Transparent Personalization
Methods based on Heterogeneous Personal Data, ExHUM at the 27th ACM Conference On User Modelling, Adaptation
And Personalization, UMAP '19. Larnaca, Cyprus.
58. Readings
• Ahn, Jae-wook, Peter Brusilovsky, Jonathan Grady, Daqing He, and Sue Yeon Syn (2007) Open user profiles
for adaptive news systems: help or harm? In the 16th international conference on World Wide Web, WWW '07, 11-20.
• Ahn, Jae-wook, Peter Brusilovsky, Daqing He, Jonathan Grady, and Qi Li.( 2008.) Personalized Web
Exploration with Task Models."In the 17th international conference on World Wide Web, WWW '08, 1-10. Beijing, China:.
• Ahn, J. and Brusilovsky, P. (2013) Adaptive visualization for exploratory information retrieval. Information Processing
and Management 49 (5), 1139–1164.
• Ahn, J., Brusilovsky, P., and Han, S. (2015) Personalized Search: Reconsidering the Value of Open User Models. In:
Proceedings of Proceedings of the 20th International Conference on Intelligent User Interfaces, Atlanta, Georgia, USA,
March 29-April 1, 2015, ACM, pp. 202-212
• Verbert, K., Parra-Santander, D., and Brusilovsky, P. (2016) Agents Vs. Users: Visual Recommendation of
Research Talks with Multiple Dimension of Relevance. ACM Transactions on Interactive Intelligent Systems 6 (2), Article
No. 11
• Parra, D. and Brusilovsky, P. (2015) User-controllable personalization: A case study with SetFusion. International
Journal of Human-Computer Studies 78, 43–67.
• Cardoso, Bruno, Gayane Sedrakyan, Francisco Gutiérrez, Denis Parra, Peter Brusilovsky, and Katrien
Verbert (2019). IntersectionExplorer, a multi-perspective approach for exploring recommendations, International
Journal of Human-Computer Studies, 121: 73-92.
• Verbert, K., Parra-Santander, D., Brusilovsky, P., Cardoso, B., and Wongchokprasitti, C. (2017) Supporting
Conference Attendees with Visual Decision Making Interfaces. In: Companion of the 22nd International Conference on
Intelligent User Interfaces (IUI '17), Limassol, Cyprus, ACM.
• Tsai, Chun-Hua and Peter Brusilovsky (2018) Beyond the Ranked List: User-Driven Exploration and Diversification
of Social Recommendation. In 23rd International Conference on Intelligent User Interfaces, 239--50. Tokyo, Japan: ACM.
72