The document discusses learning analytics and how it can be improved through semantic technologies. It defines learning analytics as the measurement, collection, analysis and reporting of data about learners and their contexts in order to understand and optimize the learning environment. It discusses challenges around collecting and analyzing data, and proposes approaches like using ontologies and linked data to make analytics more ubiquitous and context-aware. Specific tools and projects discussed include LOCO-Analyst, DEPTHS, SNAPP and linking educational data. The document advocates applying semantic web principles like open standards, distributed identity management and modularity to create personalized learning environments based on learning analytics.
Seminario eMadrid "Reinventar la educación". Seiji Isotani, University of Sao...eMadrid network
Seminario eMadrid "Reinventar la educación". Seiji Isotani, University of Sao Paulo. Advancements in Intelligent Support for Collaborative Learning. 2015-12-02. UC3M.
Intelligent tutoring systems (ITS) for online learningBrandon Muramatsu
Kurt VanLehn's presentation at Conversations on Quality: A Symposium on K-12 Online Learning hosted by MIT and the Bill and Melinda Gates Foundation, January 24-25, 2012, Cambridge, MA.
Integrating an intelligent tutoring system into a virtual worldParvati Dev
The project goal was to provide effective training to medical professionals on the SALT Triage Protocol, and to improve communication between medical professionals and military during disaster situations.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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.
Seminario eMadrid "Reinventar la educación". Seiji Isotani, University of Sao...eMadrid network
Seminario eMadrid "Reinventar la educación". Seiji Isotani, University of Sao Paulo. Advancements in Intelligent Support for Collaborative Learning. 2015-12-02. UC3M.
Intelligent tutoring systems (ITS) for online learningBrandon Muramatsu
Kurt VanLehn's presentation at Conversations on Quality: A Symposium on K-12 Online Learning hosted by MIT and the Bill and Melinda Gates Foundation, January 24-25, 2012, Cambridge, MA.
Integrating an intelligent tutoring system into a virtual worldParvati Dev
The project goal was to provide effective training to medical professionals on the SALT Triage Protocol, and to improve communication between medical professionals and military during disaster situations.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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.
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.
Learner Ontological Model for Intelligent Virtual Collaborative Learning Envi...ijceronline
An enacting approach to intelligent virtual collaborative learning model is explored through the lens of critical ontology. This ontological model enables to reuse of the domain knowledge and to make the knowledge explicitly available to the agent working as an Expert System, which uses the operational knowledge in collaborative learning environment. This ontological model used by the agent to identify the preliminary competency level of the user. This environment offers personalized education to each learner in accordance with his/her learning preferences, and learning capabilities. Here the factors considered to identify the learning capability taken are demographic profile, age, family profile, basic educational qualification and basic competency scale. The conception of heuristics is then used by the agent to determine the effectiveness of the learner by referring the different parameters of the learner available in the ontological model.To help getting over this, the paper describes the experience on using an ontological model for collaborative learning to relate and integrate the history of the learner by maintaining the history of learner in collaborative learning environment that will be used by the Multi-Objective Grey Situation Decision Making Theory to infer the understanding level of user and produces the conditional content to the user
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
Research in to Practice: Building and implementing learning analytics at TribalLACE Project
Keynote by Chris Ballard, Data Scientist, Tribal, given at the LACE SoLAR Flare event held at The Open University, Milton Keynes, UK on 9 October 2015. #LACEflare
In this modern, age of society where everyone requires individual attention to his/her self in order to gain far more than publicly gather information. Internet becomes the part of life in these circumstances when technology is much more active than any other source of communication. People need to have all information regarding their field of interest at one place stop and this could only be possible because of internet. According to a research, students engage with a lot more new information's from various sources. Particularly, students are more independent in electronic based courses than traditional way of learning courses. Although the virtual source of teaching courses are not so effective because of student unable to pay attention being as in practical classrooms but students are still progressive.
This paper is depending on the effectiveness of e-learning system in the field of education. E-learning can be perceived as a computer-learning program in which students can be taught over computer. However, today the concept of e-learning has been totally changed, it is the collection of technological sources to provide the information you required within a very short period of time. What is good e-learning process? The components and the future perspective of the e-learning program will covered in this paper.
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.
Design of a multi-agent system using the "MaSE" method for learners' metacogn...IJECEIAES
This article addresses a multi-agent approach to solving the problem of integrating metacognitive incentives into Learning Management System. The behavior of the teacher in a classroom-teaching situation, where teacher adopting the competency-based approach, is characterized by a set of didactic interventions dynamically adaptable according to the actions-reactions of the learners. These interventions are continually subjected to perfection by experience. In this article, we are interested in modeling the multi-agent system in order to help the learners develop their metacognitive skills in a continuous way. The purpose of this system is to supervise the activities and statements of the learner and communicate them to the metacognitive agent. The latter focuses on the assessment of the learner's metacognitive skills in order to trigger, automatically, metacognitive incentives to provide help messages. Integrating the agent for metacognitive control and assistance, allows learners to maintain motivation and confidence, and elicit their attention to the importance of metacognitive skills during learning activity. The "MaSE" methodology and the "agentTool" are used to model the multi-agent system.
Learning with me Mate: Analytics of Social Networks in Higher EducationDragan Gasevic
Effects of social interactions are reported in research on higher education to lead to positive outcomes such as higher levels of internalization, sense of community, academic achievement, metacognition, and student retention. The role of social networks has especially been emphasized in research due to the availability of theoretical foundations and analytic methods to investigate their effects in higher education. The increased use of technologies in education allows for the collection of large and rich datasets about social networks which call for the use of novel analytics methods. This talk will first give a brief overview of the existing work on and lessons learned from some well-known studies on social networks in higher education in diverse situations from face-to-face to massive open online courses. The talk will then identify critical challenges that require immediate attention in order for the study of social networks to make a sustainable impact on learning and teaching. The most important take away from the talk will be that
- computational aspects of the study of social networks need to be integrated deeply with theory, research and practice,
- novel methods for the study of critical dimensions (discourse, structure and dynamics) that shape network formation and network effects are necessary, and
- innovative instructional approaches are essential to address the changing conditions created by contemporary educational and technological contexts.
Learning Analytics -Towards a New Discipline-Dragan Gasevic
The talk, motivated by the present state of learning and education, identifies a need for a systematic change of the present preactice. Learning analytics is identified as a possible way to good to address this open challenge. Some connections with evidence-based medicine are drawn. Finally, learning analytics is defined as well as some open research challenges.
The project aims at developing an intelligent tutoring system, to be applied in open source learning environments, able to monitor, track, analyze and give formative assessment and feedback loop to students within the learning environment, and give inputs to tutors and teachers involved in distance learning to better their role during the process of learning. The software will be developed in java thus could be easily implemented and re-used in most of the common free platforms for eLearning.
Invited talk, INSIGHT Centre for Data Analytics, Univ. Galway, 2 Oct 2013, http://www.insight-centre.org
Abstract:
Data and analytics are transforming how organisations work in all sectors. While there are clearly ethical issues around big data and privacy, there may also be an argument that educational institutions have a moral obligation to use all the information they have to maximize the learner's progress. So, assuming education can't (arguably shouldn't) resist this revolution, the question is how to harness this new capability intelligently. Learning Analytics is an exploding research field and startup market: do leaders know what to ask when the vendors roll up with dazzling dashboards? In this talk I'll provide an overview of developments, and consider some of the key questions we should be asking. Like any modelling technology and accounting system, analytics are not neutral, and do not passively describe sociotechnical reality: they begin to shape it. Moreover, they start with the things that are easiest to count, which doesn't necessarily equate to the things we value in learning. Given the crisis in education at many levels, what realities do we want analytics to perpetuate, or bring into being?
Bio:
Simon Buckingham Shum is Professor of Learning Informatics at the UK Open University's Knowledge Media Institute. He researches, teaches and consults on Learning Analytics, Collective Intelligence and Argument Visualization. His background is B.Sc. Psychology, M.Sc. Ergonomics and Ph.D. Human-Computer Interaction. He co-edited Visualizing Argumentation (Springer 2003), the standard reference in the field, followed by Knowledge Cartography (2008). In the field of Learning Analytics, he served as Program Co-Chair of the 2nd International Learning Analytics LAK12 conference, chaired the LAK13 Discourse-Centric Learning Analytics workshop, and the LASI13 Dispositional Learning Analytics workshop. He is a co-founder of the Society for Learning Analytics Research, Compendium Institute, LearningEmergence.net, and was Co-Founder and General Editor of the Journal of Interactive Media in Education. He serves on the Advisory Groups for a variety of learning analytics initiatives in education and enterprise, and is a Visiting Fellow at University of Bristol Graduate School of Education. Contact him via http://simon.buckinghamshum.net
Designing Systemic Learning Analytics at the Open University
Belinda TynanPro-Vice-Chancellor Learning & TeachingThe Open University, UK
Simon Buckingham Shum Knowledge Media InstituteThe Open University, UK
Replay from today's webinar in the SoLAR online open course Strategy & Policy for Systemic Learning Analytics. Thanks to the Australian Office for Learning and Technology for sponsoring this, and to George Siemens for convening (replay):
Abstract: The OU has been analysing student data and feeding this back to faculties since its doors opened 40 years ago. However, the emergence of learning analytics technologies open new possibilities for engaging in more effective sensemaking of richer learner data, and more timely interventions. We will introduce the framework we are developing to orchestrate the rollout of a systemic organisational analytics infrastructure (both human and technical), and discuss some of the issues that arise. We will also describe how strategic research efforts will key into this design, should they prove effective.
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.
Learner Ontological Model for Intelligent Virtual Collaborative Learning Envi...ijceronline
An enacting approach to intelligent virtual collaborative learning model is explored through the lens of critical ontology. This ontological model enables to reuse of the domain knowledge and to make the knowledge explicitly available to the agent working as an Expert System, which uses the operational knowledge in collaborative learning environment. This ontological model used by the agent to identify the preliminary competency level of the user. This environment offers personalized education to each learner in accordance with his/her learning preferences, and learning capabilities. Here the factors considered to identify the learning capability taken are demographic profile, age, family profile, basic educational qualification and basic competency scale. The conception of heuristics is then used by the agent to determine the effectiveness of the learner by referring the different parameters of the learner available in the ontological model.To help getting over this, the paper describes the experience on using an ontological model for collaborative learning to relate and integrate the history of the learner by maintaining the history of learner in collaborative learning environment that will be used by the Multi-Objective Grey Situation Decision Making Theory to infer the understanding level of user and produces the conditional content to the user
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
Research in to Practice: Building and implementing learning analytics at TribalLACE Project
Keynote by Chris Ballard, Data Scientist, Tribal, given at the LACE SoLAR Flare event held at The Open University, Milton Keynes, UK on 9 October 2015. #LACEflare
In this modern, age of society where everyone requires individual attention to his/her self in order to gain far more than publicly gather information. Internet becomes the part of life in these circumstances when technology is much more active than any other source of communication. People need to have all information regarding their field of interest at one place stop and this could only be possible because of internet. According to a research, students engage with a lot more new information's from various sources. Particularly, students are more independent in electronic based courses than traditional way of learning courses. Although the virtual source of teaching courses are not so effective because of student unable to pay attention being as in practical classrooms but students are still progressive.
This paper is depending on the effectiveness of e-learning system in the field of education. E-learning can be perceived as a computer-learning program in which students can be taught over computer. However, today the concept of e-learning has been totally changed, it is the collection of technological sources to provide the information you required within a very short period of time. What is good e-learning process? The components and the future perspective of the e-learning program will covered in this paper.
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.
Design of a multi-agent system using the "MaSE" method for learners' metacogn...IJECEIAES
This article addresses a multi-agent approach to solving the problem of integrating metacognitive incentives into Learning Management System. The behavior of the teacher in a classroom-teaching situation, where teacher adopting the competency-based approach, is characterized by a set of didactic interventions dynamically adaptable according to the actions-reactions of the learners. These interventions are continually subjected to perfection by experience. In this article, we are interested in modeling the multi-agent system in order to help the learners develop their metacognitive skills in a continuous way. The purpose of this system is to supervise the activities and statements of the learner and communicate them to the metacognitive agent. The latter focuses on the assessment of the learner's metacognitive skills in order to trigger, automatically, metacognitive incentives to provide help messages. Integrating the agent for metacognitive control and assistance, allows learners to maintain motivation and confidence, and elicit their attention to the importance of metacognitive skills during learning activity. The "MaSE" methodology and the "agentTool" are used to model the multi-agent system.
Learning with me Mate: Analytics of Social Networks in Higher EducationDragan Gasevic
Effects of social interactions are reported in research on higher education to lead to positive outcomes such as higher levels of internalization, sense of community, academic achievement, metacognition, and student retention. The role of social networks has especially been emphasized in research due to the availability of theoretical foundations and analytic methods to investigate their effects in higher education. The increased use of technologies in education allows for the collection of large and rich datasets about social networks which call for the use of novel analytics methods. This talk will first give a brief overview of the existing work on and lessons learned from some well-known studies on social networks in higher education in diverse situations from face-to-face to massive open online courses. The talk will then identify critical challenges that require immediate attention in order for the study of social networks to make a sustainable impact on learning and teaching. The most important take away from the talk will be that
- computational aspects of the study of social networks need to be integrated deeply with theory, research and practice,
- novel methods for the study of critical dimensions (discourse, structure and dynamics) that shape network formation and network effects are necessary, and
- innovative instructional approaches are essential to address the changing conditions created by contemporary educational and technological contexts.
Learning Analytics -Towards a New Discipline-Dragan Gasevic
The talk, motivated by the present state of learning and education, identifies a need for a systematic change of the present preactice. Learning analytics is identified as a possible way to good to address this open challenge. Some connections with evidence-based medicine are drawn. Finally, learning analytics is defined as well as some open research challenges.
The project aims at developing an intelligent tutoring system, to be applied in open source learning environments, able to monitor, track, analyze and give formative assessment and feedback loop to students within the learning environment, and give inputs to tutors and teachers involved in distance learning to better their role during the process of learning. The software will be developed in java thus could be easily implemented and re-used in most of the common free platforms for eLearning.
Invited talk, INSIGHT Centre for Data Analytics, Univ. Galway, 2 Oct 2013, http://www.insight-centre.org
Abstract:
Data and analytics are transforming how organisations work in all sectors. While there are clearly ethical issues around big data and privacy, there may also be an argument that educational institutions have a moral obligation to use all the information they have to maximize the learner's progress. So, assuming education can't (arguably shouldn't) resist this revolution, the question is how to harness this new capability intelligently. Learning Analytics is an exploding research field and startup market: do leaders know what to ask when the vendors roll up with dazzling dashboards? In this talk I'll provide an overview of developments, and consider some of the key questions we should be asking. Like any modelling technology and accounting system, analytics are not neutral, and do not passively describe sociotechnical reality: they begin to shape it. Moreover, they start with the things that are easiest to count, which doesn't necessarily equate to the things we value in learning. Given the crisis in education at many levels, what realities do we want analytics to perpetuate, or bring into being?
Bio:
Simon Buckingham Shum is Professor of Learning Informatics at the UK Open University's Knowledge Media Institute. He researches, teaches and consults on Learning Analytics, Collective Intelligence and Argument Visualization. His background is B.Sc. Psychology, M.Sc. Ergonomics and Ph.D. Human-Computer Interaction. He co-edited Visualizing Argumentation (Springer 2003), the standard reference in the field, followed by Knowledge Cartography (2008). In the field of Learning Analytics, he served as Program Co-Chair of the 2nd International Learning Analytics LAK12 conference, chaired the LAK13 Discourse-Centric Learning Analytics workshop, and the LASI13 Dispositional Learning Analytics workshop. He is a co-founder of the Society for Learning Analytics Research, Compendium Institute, LearningEmergence.net, and was Co-Founder and General Editor of the Journal of Interactive Media in Education. He serves on the Advisory Groups for a variety of learning analytics initiatives in education and enterprise, and is a Visiting Fellow at University of Bristol Graduate School of Education. Contact him via http://simon.buckinghamshum.net
Designing Systemic Learning Analytics at the Open University
Belinda TynanPro-Vice-Chancellor Learning & TeachingThe Open University, UK
Simon Buckingham Shum Knowledge Media InstituteThe Open University, UK
Replay from today's webinar in the SoLAR online open course Strategy & Policy for Systemic Learning Analytics. Thanks to the Australian Office for Learning and Technology for sponsoring this, and to George Siemens for convening (replay):
Abstract: The OU has been analysing student data and feeding this back to faculties since its doors opened 40 years ago. However, the emergence of learning analytics technologies open new possibilities for engaging in more effective sensemaking of richer learner data, and more timely interventions. We will introduce the framework we are developing to orchestrate the rollout of a systemic organisational analytics infrastructure (both human and technical), and discuss some of the issues that arise. We will also describe how strategic research efforts will key into this design, should they prove effective.
Perspectives on Sustainability in Higher Education: Inviting and Leveraging C...BCcampus
Vivian Neal, Educational Consultant, Teaching and Learning Centre, Simon Fraser University
Janet Pivnick, Educational Consultant, Teaching and Learning Centre, Simon Fraser University
Festival of Learning in Burnaby, B.C. - June 6-9, 2016
Webinar for LearningAnalytics.net Open Course, Feb. 2011, (Athabasca U)
Simon Buckingham Shum
Knowledge Media Institute
Open University UK
http://simon.buckinghamshum.net
http://open.edu
Keynote Address, Expanding Horizons 2012, Macquarie University
http://staff.mq.edu.au/teaching/workshops_programs/expanding_horizons
"Learning Analytics": unprecedented data sets and live data streams about learners, with computational power to help make sense of it all, and new breeds of staff who can talk predictive models, pedagogy and ethics. This means rather different things to different people: unprecedented opportunity to study, benchmark and improve educational practice, at scales from countries and institutions, to departments, individual teachers and learners. "Benchmarking" may trigger dystopic visions of dumbed down proxies for 'real teaching and learning', but an emu response is no good. For educational institutions, our calling is to raise the quality of debate, shape external and internal policy, and engage with the companies and open communities developing the future infrastructure. How we deploy these new tools rests critically on assessment regimes, what can be logged and measured with integrity, and what we think it means to deliver education that equips citizens for a complex, uncertain world.
Will Learning Analytics Transform Higher Education?Abelardo Pardo
Discussion on the elements, actors, cultural change and scenarios that are related to Learning Analytics in Higher Education Institutions. Presentation given at the Digital Education Show Asia, Kuala Lumpur, June 2015
Keynote delivered by George Siemens (@gsiemens), Dragan Gasevic (@dgasevic), and Ryan Baker (@BakerEDMLab) at the 8th International Educational Data Mining Conference (EDM 2015) in Madrid, Spain on June 27, 2015
Educational data mining and learning analytics have to date largely focused on specific research questions that provide insight into granular interactions. These insights have bee abstracted to include the development of predictive models, intelligent tutors, and adaptive learning. While there are several domains where holistic or systems models have provided additional explanatory power, work around learning has not created holistic models with the level of concreteness or richness required. The need for both granular and integrated high-level view of learning is further influenced by distributed, life long, multi-spaced learning that today defines education. Drawing on social and knowledge graph theory, we propose the development of a Personal Learning Graph (PLeG) - an open and learner-owned profile that addresses cognitive, affective, and related elements that reflect what a learner knows, is able to do, and processes through which she learns best. This talk will introduce PLeG, detail required technical infrastructure, and articulate how it would interact with established learning software.
Learning analytics: study goal and data explorerJisc
Presenters:
Rob Wyn Jones, senior data and analytics integrator, Jisc
Paul Bailey, senior co-design manager, Jisc
An introduction to two tools from the Jisc learning analytics service.
Study goal provides a motivational student learning app to view and record analytics data and is available on Apple and Android.
Data explorer provides a simple set of admin tools to assist HEI and FEI’s onboarding into the learning records warehouse, and paves the way for more sophisticated dashboards and analysis, from a range of vendors and open source, to be adopted through our new framework.
Human-Centered Learning Analytics and Artificial Intelligence in Education: H...Yannis
Although Artificial Intelligence (AI) and Learning Analytics (LA) have shown their potential in Education, stakeholders’ agency seems to be threatened. On the other hand, multiple issues regarding FATE (Fairness, Accountability, Transparency and Ethics) have been raised when AI or LA-based solutions are designed and implemented. These issues have been especially acute since the emergence of Large Language Models and Generative AI.
This talk discusses the quest for an optimal balance between human and computational agents, when LA tools and services are employed in a Technology Enhanced Learning (TEL) ecosystem. Through the discussion of relevant conceptual models and examples, it argues for Human-Centered Learning Analytics (HCLA) and Human-Centered Artificial Intelligence (HCAI) approaches, where agency and FATE principles are essential design parameters.
The talk focuses especially on LA/AI solutions that may position teachers as designers of effective interventions and orchestration actions. Selected Human-Centered Design (HCD) principles are discussed and illustrated, and directions for future research and development are formulated to overcome the main obstacles for adoption of human-centered approaches for LA and AI in education.
From institutional Policy to individual practice: Using Learning technologies...Sarra_Saffron_Powell
Charting the development and rationale of a student learning skills project in Higher Education as an integrated semi automated system that uses learner diagnostics to provide automated learning plans for students. Looks at using Policy as institutional leverage and technology to assess student learning skills development.
Bridging the gap of the educational system across different countries through...PhD Assistance
The gap in the educational system has been a major drawback globally. The idea and concept of E-Learning have been evolved as a result of many kinds of Research. E-learning has assisted in closing this gap. The main goal of the study is to offer quality education through e-learning by assessing the effectiveness of e-learning mode. The focus has been to assess the e-learning potential to provide a quality education through electronic means and also to evaluate the scope of e-learning. E-learning provides a better standard of living for students across the world. This paper deals with improving the student’s quality of education and their standard of living
Visite : https://www.phdassistance.com/blog/
Contact Us:
UK NO: +44-1143520021
India No: +91-8754446690
Email: info@phdassistance.com
Tools and Evaluation Techniques to Support Social Awareness in CSCeL: The AV...Niki Lambropoulos PhD
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
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
JISC RSC London Workshop - Learner analyticsJames Ballard
Introduction to learning analytics and approaches to learner engagement to raise awareness and set the seen for upcoming projects and advice for supported learning providers.
Educational Data Mining/Learning Analytics issue brief overviewMarie Bienkowski
An overview of the Draft Issue Brief prepared by SRI International for the US Department of Education on Educational Data Mining and Learning Analytics
In the current digital era, education system has witness tremendous growth in data storage and efficient retrieval. Many Institutes have very huge databases which may be of terabytes of knowledge and information. The complexity of the data is an important issue as educational data consists of structural as well as non-structural type which includes various text editors like node pad, word, PDF files, images, video, etc. The problem lies in proper storage and correct retrieval of this information. Different types of learning platform like Moodle have implemented to integrate the requirement of educators, administrators and learner. Although this type of platforms are indeed a great support of educators, still mining of the large data is required to uncover various interesting patterns and facts for decision making process for the benefits of the students. In this research work, different data mining classification models are applied to analyse and predict students’ feedback based on their Moodle usage data. The models described in this paper surely assist the educators, decision maker, mentors to early engage with the issues as address by students. In this research, real data from a semester has been experimented and evaluated. To achieve the better classification models, discretization and weight adjustment techniques have also been applied as part of the pre – processing steps. Finally, we conclude that for efficient decision making with the student’s feedback the classifier model must be appropriate in terms of accuracy and other important evaluation measures. Our experiments also shows that by using weight adjustment techniques like information gain and support vector machines improves the performance of classification models.
In the current digital era, education system has witness tremendous growth in data storage and efficient retrieval. Many Institutes have very huge databases which may be of terabytes of knowledge and information. The complexity of the data is an important issue as educational data consists of structural as well as non-structural type which includes various text editors like node pad, word, PDF files, images, video, etc. The problem lies in proper storage and correct retrieval of this information. Different types of learning platform like Moodle have implemented to integrate the requirement of educators, administrators and learner. Although this type of platforms are indeed a great support of educators, still mining of the large data is required to uncover various interesting patterns and facts for decision making process for the
benefits of the students.
Learning, design and technology developmental evaluation and the experience api Charles Darwin University
Learning, design and technology developmental evaluation and the experience api. Invited presentation to Global Mindset 12th thought leading conference on Assessment and Learning on 29 Oct 2014.The conference is all about students and teachers and how they can improve learning through better understanding of:
- current state of assessment and learning
- future of assessment and learning
The keynote is by Eric Mazur, Professor Physics Harvard, recipient of Minerva Prize.
Nurturing the Connections: The Role of Quantitative Ethnography in Learning A...Dragan Gasevic
This talk will explore connections between two emerging fields focused on harnessing the potential of data – learning analytics and quantitative ethnography. Learning analytics is focused on the analysis of data collected from user interactions with technology with the goal of advancing our understanding of and enhancing human learning. Despite some early success stories and widespread interest, producing meaningful and actionable results is still a top open research challenge for learning analytics. The talk will first explore how quantitative ethnography can offer promising approaches that can address this open challenge in learning analytics. The talk will next discuss how progress in learning analytics can be used to accelerate the development of the field of quantitative ethnography. The talk will finally outline promising directions for future research at the intersection of learning analytics and quantitative ethnography.
Can learning analytics offer meaningful assessment? Dragan Gasevic
The emergence of learning analytics afforded for the analysis of digital traces of user interaction with technology. This analysis offers many opportunities to advance understanding and enhance learning and the environments in which learning occurs. Existing research has shown how learning analytics can provide contributions to different areas of education such as prediction of student success, uncovering learning strategies, understanding affective states, and unpacking the role social networks in learning. While these results have shown much promise, one critical challenge remains unclear – how learning analytics can help track learning progression and inform assessment especially from the perspective of the 21st century skills. This talk will explore opportunities and challenges for the integration of methods commonly used in learning analytics to analyze different digital traces with methods commonly used in assessment. The talk particularly focuses on open learning environments where analytics-based assessment is rather underexplored in contrast to assessment in specialized (intelligent tutoring) systems where the combined use of data mining and assessment has been established for some time now.
Towards Strengthening Links between Learning Analytics and AssessmentDragan Gasevic
. The emergence of learning analytics afforded for the analysis of digital traces of user interaction with technology. This analysis offers many opportunities to advance understanding and enhance learning and the environments in which learning occurs. Existing research has shown how learning analytics can provide contributions to different areas of education such as prediction of student success, uncovering learning strategies, understanding affective states, and unpacking the role social networks in learning. While these results have shown much promise, one critical challenge remains unclear – how learning analytics can help track learning progression and inform assessment especially from the perspective of the 21st century skills. This talk will explore opportunities and challenges for the integration of methods commonly used in learning analytics to analyze different digital traces with methods commonly used in assessment and psychometric research. The paper particularly focuses on open learning environments where analytics-based assessment is rather underexplored in contrast to assessment in specialized (intelligent tutoring) systems where the combined use of data mining and psychometric techniques has been established for some time now.
Let’s get there! Towards policy for adoption of learning analyticsDragan Gasevic
The field learning analytics is established with the promise for the education sector to embrace the use of data for decision making. There are many examples of successful use of learning analytics to enhance student experience, increase learning outcomes, and optimize learning environments. Despite much interest in learning analytics, many higher education institutions are still looking for effective ways that can enable systemic uptake. The talk will first describe some selected examples of the successful use of learning analytics in higher education. Key challenges identified to affect implementation of learning analytics will then be discussed. This will be followed with an overview of an approach to the development of institutional policy and strategy for the learning analytics implementation in higher education. The talk will be based on the findings of several international studies and will critically interrogate the role of institutional and cultural differences.
State and Directions of Learning Analytics Adoption (Second edition)Dragan Gasevic
The analysis of data collected from user interactions with educational and information technology has attracted much attention as a promising approach for advancing our understanding of the learning process. This promise motivated the emergence of the new field learning analytics and mobilized the education sector to embrace the use of data for decision-making. This talk will first introduce the field of learning analytics and touch on lessons learned from some well-known case studies. The talk will then identify critical challenges that require immediate attention in order for learning analytics to make a sustainable impact on learning, teaching, and decision making. The talk will conclude by discussing a set of milestones selected as critical for the maturation of the field of learning analytics. The most important take away from the talk will be that
- systemic approaches to the development and adoption of learning analytics are critical,
- multidisciplinary teams are necessary to unlock a full potential of learning analytics, and
- capacity development at institutional levels through the inclusion of diverse stakeholders is essential for full learning analytics adoption.
This is the second edition of the talk that previously gave under the same title on several occasions. The second edition reflects many developments happened in the field of learning analytics, especially those in the following two projects - http://he-analytics.com and http://sheilaproject.eu.
Technologies to support self-directed learning through social interactionDragan Gasevic
This talk will describe underlying principles, design, and experience gained with ProSolo, a platform that supports personalized, competency-based learning through social interaction. Traditional educational models are primarily focused on classroom education and training typically associated with the notion of credit hours as the (only) route towards formal credentials. This limits opportunities for creating personalized learning pathways in the changing educational context. ProSolo provide users with the ability to unbundle education programs, courses, and units into discrete yet inter-related competencies, allowing learners to construct their education pathway in a manner that better reflects their interests and future career motivations and requirements. ProSolo is developed with the intention of providing learners with opportunities to customize, modify, and personalize their self-directed learning journey. ProSolo supports the development of skills for self-directed learning by allowing learners to control the planning, learning, and presentation of outcomes associated with their learning. To support learners with different levels of prior knowledge, study skills, and cultural backgrounds, ProSolo offers features for supporting self-directed learning through three types of scaffolds, including instructional, social, and technological. Learning in ProSolo occurs within a socially rich environment that aggregates learners’ information created and shared in their existing online spaces. ProSolo makes use of learning analytics to empower learners and instructors in this new model of education. ProSolo was used in the Data, Learning, and Analytics MOOC and is currently being piloted at several university sites.
Learning analytics: An opportunity for higher education?Dragan Gasevic
Slides used in my keynote at the Annual Conference of the European Association of Distance Teaching Universities - The open, online, flexible higher education conference - #OOFHEC2015
Learning analytics are more than measurementDragan Gasevic
Slides used for the keynote
Learning analytics are more than measurement
at
Policies for Educational Data Mining and Learning Analytics Briefing
organized by http://www.laceproject.eu/
Social network analysis and understanding of massive open online coursesDragan Gasevic
This presentation is prepared for DALMOOC and talks about the use of social network analysis for understanding distributed pedagogies in massive open online courses (MOOCs). The presentation is based on
Skrypnyk, O., Joksimović, S. Kovanović, V., Gasevic, D., Dawson, S. (2014). Roles of course facilitators, learners, and technology in the flow of information of a cMOOC. British Journal of Educational Technology (submitted), http://www.sfu.ca/~dgasevic/papers_shared/bjet2014_cmoocs.pdf.
Social network analysis and social presenceDragan Gasevic
This presentation is prepared for DALMOOC and talks about the use of social network analysis for the development of social capital based on social presence in communities of inquiry The presentation is based on
Kovanović, V., Joksimović, S., Gašević, D., Hatala, M., “What is the source of social capital? The association between social network position and social presence in communities of inquiry,” In Proceedings of 7th International Conference on Educational Data Mining – Workshops, London, UK, 2014, http://ceur-ws.org/Vol-1183/gedm_paper03.pdf
Social network analysis and learning designDragan Gasevic
This presentation is prepared for DALMOOC and talks about the use of social network analysis for improvement of learning design. The presentation is based on
Lockyer, L., Heathcote, E., & Dawson, S. (2013). Informing pedagogical action: Aligning learning analytics with learning design. American Behavioral Scientist, 57(10), 1439-1459, doi:10.1177/0002764213479367
Social network analysis and creative potentialDragan Gasevic
This presentation is prepared for DALMOOC and talks about the use of social network analysis for understanding of creative potential. The presentation is based on
Dawson, S., Tan, J. P. L., & McWilliam, E. (2011). Measuring creative potential: Using social network analysis to monitor a learners' creative capacity. Australasian Journal of Educational Technology, 27(6), 924-942.
Social network analysis and academic performanceDragan Gasevic
This presentation is prepared for DALMOOC and talks about the use of social network analysis for understanding and prediction of academic performance. The presentation is based on
Gašević, D., Zouaq, A., Jenzen, R. (2013) 'Choose your Classmates, your GPA is at Stake!' The Association of Cross-Class Social Ties and Academic Performance. American Behavioral Scientist, 57(10), 1459-1478, http://www.sfu.ca/~dgasevic/papers_shared/abs2013.pdf
Network modularity and community identificationDragan Gasevic
The presentation describes the notion of network modularity as a method used for identification of communities in social network analysis. The presentation is prepared by Dragan Gasevic for DALMOOC.
Network measures used in social network analysis Dragan Gasevic
Definition of measures (diameter, density, degree centrality, in-degree centrality, out-degree centrality, betweenness centrality, closeness centrality) used in social network analysis. The presentation is prepared by Dragan Gasevic for DALMOOC.
This presentation define network structure and commonly used sources for data collection in social network analysis. The presentation is prepared for DALMOOC by Dragan Gasevic.
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
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.
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.
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
11. Three Generation of
Distance Education Pedagogies
Anderson, T. & Dron, J. (2011) Three Generations of Distance Education Pedagogy, International
Review of Research in Open and Distance Learning 12(3), 80-97, http://goo.gl/j3mRF
23. Linked Data
http://richard.cyganiak.de/2007/10/lod/
24. Linked Data
http://richard.cyganiak.de/2007/10/lod/
25. “A crazy problem requires
a crazy solution!”
(Griff Richards, 2005)
26. Learning Context Ontology:
LOCO
Jovanovic, J., Knight, C., Gasevic, D., Richards, G., "Ontologies for Effective Use of Context in e-Learning Settings," Educational Technology &
Society, Vol. 10, No. 3, 2007, pp. 47-59
27. LOCO-Analyst
OAST and LOCO-Analyst
Ali, L., Hatala, M. Gašević, D., Jovanović, J., "A Qualitative Evaluation of Evolution of a Learning Analytics
Tool," Computers & Education, Vol. 58, No. 1, 2012, pp. 470-489, http://goo.gl/lCvMT
28. LOCO-Analyst
OAST and LOCO-Analyst
Ali, L., Hatala, M. Gašević, D., Jovanović, J., "A Qualitative Evaluation of Evolution of a Learning Analytics
Tool," Computers & Education, Vol. 58, No. 1, 2012, pp. 470-489, http://goo.gl/lCvMT
30. Formative Evaluation
Category Sub-category Q8
Visualization/GUI 77.8%
Suggestions for improving Annotations 5.66%
Other Features 11.11%
Data Visualization -
No suggestions but liked Interface Design 5.56%
Annotations -
Ali, L., Hatala, M. Gašević, D., Jovanović, J. (2012). A Qualitative Evaluation of Evolution of
a Learning Analytics Tool. Computers & Education, 58(1) 470-489, http://goo.gl/lCvMT
31. Learning Analytics
What and how to report?
Measurement, collection, analysis, and reporting of data
about learners and their contexts
39. Learning Analytics Acceptance Model
Inspired by Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D., 2003. User acceptance of
information technology: Toward a unified view. MIS Quarterly, (27:3), pp. 425-478.
40. Learning Analytics Acceptance Model
Ali, L., Asadi, M., Jovanović, J., Gašević, D., Hatala, M., “Factors influencing Perceived Utility and Adoption of
a Learning Analytics Tool: An Empirical Study,” Computers & Education (submitted)
43. Learning Analytics
What to measure?
Measurement, collection, analysis, and reporting of data
about learners and their contexts
44.
45. Learning Analytics for
Community of Inquiry
Effects of
instructional interventions
Example: Role playing (invited expert and moderation) with
explicit instructions how to contribute
46. Social Network Analytics in
the Community of Inquiry
Just information sharing
does not mean a central role
47. Social Network Analytics
Performance prediction based
on joint course enrollment
Example: Degree, between centrality and closeness centrality
explain ~46% of GPA
49. Social Learning Analytics for
Self-regulated Workplace Learning
Siadaty, M., Gašević, D., Jovanović, J., Milikić, N., Jeremić, N., Ali, L., Giljanović, A., Hatala, M., "Learn-B:
Social Analytics-enabled Tool for Self-regulated Workplace Learning," In Proceedings of the 2nd
International Conference on Learning Analytics and Knowledge, 2012, http://goo.gl/Vm8tv
50. Social Learning Analytics for
Self-regulated Workplace Learning
Siadaty, M., Gašević, D., Jovanović, J., Milikić, N., Jeremić, N., Ali, L., Giljanović, A., Hatala, M., "Learn-B:
Social Analytics-enabled Tool for Self-regulated Workplace Learning," In Proceedings of the 2nd
International Conference on Learning Analytics and Knowledge, 2012, http://goo.gl/Vm8tv
51. Social Learning Analytics for
Self-regulated Workplace Learning
Siadaty, M., Gašević, D., Jovanović, J., Milikić, N., Jeremić, N., Ali, L., Giljanović, A., Hatala, M., "Learn-B:
Social Analytics-enabled Tool for Self-regulated Workplace Learning," In Proceedings of the 2nd
International Conference on Learning Analytics and Knowledge, 2012, http://goo.gl/Vm8tv
52. DEPTHS
DEsign Patterns Teaching Help System
Project-based learning
Self-regulation and community of inquiry
http://op4l.fon.bg.ac.rs/op4l_services
55. LOD-based Personal Learning
Environments: Principles
Integration of distributed and heterogeneous data sources, tools and services
Principle 1:
Quintessential for the realization of all other principles, and thus development of advanced PLEs, i.e., PLEs
Integration
offering context-aware and personalized learning, as well as ubiquitous data access
Open standards => application and device independence, long-term access to content and services,
interoperability
Principle 2: Openness
Open source software => cost-effective customizations to the users’ needs,
Open content => more diverse and constantly evolving and improving educational content
The users’ ability to:
Principle 3: - seamlessly access different tools/services that are part of their PLEs;
Distributed Identity - pull together their profile data from those tools/services;
Management
- regulate the use of their data within tools/services that from their PLEs.
Improved efficiency of user’s interactions with the environment through capturing and leveraging data about
Principle 4: the user's learning context;
Context-awareness Improvements: higher quality of search results, proactive recommendations, mediation of
communication/collaboration
The ability to seamlessly “configure” a PLE for any given purpose (i.e., learning goal), by adding new and/or
Principle 5: Modularity replacing existing content, tools and/or services
Support for standardized and light-weight approaches for the development of dynamic (e-learning) mashups.
Principle 6: Ubiquitous Seamless access to and integration of profile data, data about learning activities and learning resources
data access Ability to access and use relevant resources regardless of the system/tool/service the user is currently using
The ‘user at the centre’ paradigm – student is responsible for managing his/her individual knowledge and
Principle 7: competences
User Centricity The learning system is the facilitator: it identifies the appropriate resources, adapts them to the user’s learning
context, and suggests the most appropriate learning strategies
Jeremić, Z., Jovanović, J., Gašević, D., "Personal Learning Environments on the Social Semantic
Web," Semantic Web Journal, 2012 (in press), http://goo.gl/yaqQN
56. Learning Analytics
How to analyze?
Measurement, collection, analysis, and reporting of data
about learners and their contexts
57. Measuring Cognitive Presence
Text mining and linked data
A very similar text-mining problem is spam classification.
Sure, sounds funny, but computing is a strange affair !