Monitoring Online Courses with Logfiles. Research.moodle.net/pluginfile.php/140/mod_resource/content/2/Full Proceedings.pdf. Evolutionary algorithms for subgroup discovery in e-learning: A practical application using Moodle data. Abstract This work describes the application of subgroup discovery using evolutionary algorithms to the usage data of the Moodle course management system, a case study of the University of Cordoba, Spain.
The objective is to obtain rules which describe relationships between the student’s usage of the different activities and modules provided by this e-learning system and the final marks obtained in the courses. We use an evolutionary algorithm for the induction of fuzzy rules in canonical form and disjunctive normal form. The results obtained by different algorithms for subgroup discovery are compared, showing the suitability of the evolutionary subgroup discovery to this problem. Keywords Web-based education; Subgroup discovery; Evolutionary algorithms; Fuzzy rules Copyright © 2007 Elsevier Ltd. Information Technology at Purdue. Research on the use of analytics to increase student success, and the implementation of analytics through Course Signals, has been conducted by Purdue staff associated with ITaP.
The references below are part of a broader body of literature on academic analytics, and many of them are cited as the foundation for others' research. As more research is published, this listing will be updated. Academic Analytics Campbell, J. P. (2007) proceedings from the higher learning commission: Academic success;" href="/assets/docs/learning/research/ELI7059.pdf"> View PDFCampbell, J. Course Signals. Www.itap.purdue.edu/learning/docs/research/Arnold_Pistilli-Purdue_University_Course_Signals-2012.pdf. "Utilizing student data within the course management system to determin" by John Patrick Campbell.
John Patrick Campbell, Purdue University Abstract For nearly six decades, researchers have been studying the issues of student persistence and retention in higher education.
Despite the decades of research and projects to improve retention, overall retention figures have remained between 45% and 50%. With the contentious debates over the Higher Education Reauthorization Act in 2005 and increasingly constrained federal and state budgets, the demand for increased accountability from the students, families, and the legislature has required higher education institutions to renew their focus on issues of academic quality, cost effectiveness, student retention, and graduation rates. ^ This research expands traditional retention and academic success studies by introducing additional student behavioral data from the course management system (CMS). Degree. Learning, networks, knowledge, technology, community. Data Cookbook - Public Area - Organization: Predictive Analytics Reporting Framework.
Net.educause.edu/ir/library/pdf/PUB9012. About LOCO-Analyst. Quick overview of the LOCO-Analyst tool ... is based on the notion of Learning Object Context The generation of feedback in LOCO-Analyst is based on analysis of the user tracking data.
These analyses are based on the notion of Learning Object Context (LOC) which is about a student (or a group of students) interacting with a learning content by performing certain activity (e.g. reading, quizzing, chatting) with a particular purpose in mind. The purpose of LOC is to facilitate abstraction of relevant concepts from user-tracking data of various e-learning systems and tools. ... uses Semantic Web technologies It is built on top of the LOCO (Learning Object Context Ontologies) ontological framework, which we developed to enable formal representation of the LOC data. ... extends Reload Content Packaging Editor LOCO-Analyst is implemented as an extension of Reload Content Packaging Editor, an open-source tool for creating courses compliant with the IMS Content Packaging (IMS CP) specification.
Www.e-ucm.es/drafts/e-UCM_draft_199.pdf. Ac.els-cdn.com/S0360131509002486/1-s2.0-S0360131509002486-main.pdf?_tid=2c85077e-9891-11e3-b080-00000aacb35d&acdnat=1392723683_6dfa0e92caf7a29626f60aa44415c762. Signals: Applying Academic Analytics. Key Takeaways Applying the principles of business intelligence analytics to academia promises to improve student success, retention, and graduation rates and demonstrate institutional accountability.
The Signals project at Purdue University has delivered early successes in academic analytics, prompting additional projects and new strategies. Significant challenges remain before the predictive nature of academic analytics meets its full potential. Academic analytics helps address the public’s desire for institutional accountability with regard to student success, given the widespread concern over the cost of higher education and the difficult economic and budgetary conditions prevailing worldwide.
Purdue University’s Signals project applies the principles of analytics widely used in business intelligence circles to the problem of improving student success within a course and, hence, improving the institution’s retention and graduation rates over time. Toward Accountability. Nces.ed.gov/npec/pdf/kuh_team_report.pdf. Harnessing the Power of Technology, Openness, and Collaboration (EDUCAUSE Quarterly) Key Takeaways Supporting students at risk of not completing a course and ultimately, perhaps, of not completing college requires targeted data, guided intervention, and reduced costs.
Combining open educational resources, open academic analytics, and open-source software can address systemic challenges to college success and completion. The Kaleidoscope Project is evaluating, adopting, and enhancing open educational resources for general education courses, while the Open Academic Analytics Initiative is developing an open-source ecosystem for academic analytics. Together, Kaleidoscope and OAAI could facilitate a cost-effective systemic approach to address the challenge of college completion.
As Lorena steps into her Biology 101 classroom for the first time, she feels a combination of excitement and uneasiness. The following week Professor Halyard, seeing the notice from the registrar about Lorena’s withdrawal, wishes he had connected with her earlier in the semester. Net.educause.edu/ir/library/pdf/NGI1301. Ac.els-cdn.com/S0360131509002486/1-s2.0-S0360131509002486-main.pdf?_tid=57a48a40-980c-11e3-a7ee-00000aab0f02&acdnat=1392666632_e96493ead7e1649ba1452f0b7dd4a082. LAK Dataset & Challenge. Penetrating the Fog: Analytics in Learning and Education.
George Siemens (firstname.lastname@example.org) is with the Technology Enhanced Knowledge Research Institute at Athabasca University.
Phil Long (email@example.com) is a Professor in the Schools of ITEE and Psychology and is Director of the Centre for Educational Innovation & Technology at the University of Queensland. Comments on this article can be posted to the web via the link at the bottom of this page. Attempts to imagine the future of education often emphasize new technologies—ubiquitous computing devices, flexible classroom designs, and innovative visual displays. But the most dramatic factor shaping the future of higher education is something that we can’t actually touch or see: big data and analytics. Basing decisions on data and evidence seems stunningly obvious, and indeed, research indicates that data-driven decision-making improves organizational output and productivity.1 For many leaders in higher education, however, experience and “gut instinct” have a stronger pull. Data Explosion Notes. The State of Educational Data Mining in 2009. Academic analytics. Lak12 - home.
The State of Educational Data Mining in 2009: A Review and Future Visions. DataShop > Research Goals. I'm a Show all topics Data miner/computer scientist Cognitive scientist ITS/AIED researcher User modeling researcher Educational psychologist Course developer Psychometrician Learning analytics researcher and I want to ...
Analyze process data from an experiment Many hypotheses on learning are tested through in vivo experimentation with data stored in DataShop. Within DataShop, users can create samples on subsets of data and compare different conditions within the data. You can see examples of the kinds of analyses that researchers have performed by clicking on the show related datasets and papers link below and reading one of those papers. Error rates, times, and hints can also be viewed by condition in learning curves or the performance profiler by creating samples for each condition and selecting those.
Improve student learning in my system There are many ways DataShop can help you analyze your dataset to try to discover ways you might improve student learning from your system.