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Learning Analytics and MOOCs

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HCII 2020, Kopenhagen, Denmark (online)

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Learning Analytics and MOOCs

  1. 1. LEARNING ANALYTICS AND MOOCS Ebru İnan and Martin Ebner Educational Technology, Graz University of Technology, Graz, Austria einan@student.tugraz.at, martin.ebner@tugraz.at
  2. 2. Outlines § 1. Introduction • Aim of Study • Learning Analytics • MOOCs § 2. Research Method § 3. Results of the Literature Rewiew § Data Categories § Typcal Examples § Advantages and Challenges § 4. Discussions/Conclusions
  3. 3. 1. Introduction v Educational technologies in the 21st century v Technology –Enhanced Learning v Learning Analytics v MOOCs
  4. 4. Purpose Ø«It is aimed to present an overview of existing learning analytics concepts and methods strongly focused in conjunction with Massive Open Online Courses (MOOCs)» ØThe Methods ØBenefits and Challegens
  5. 5. What is the Learning Analytics? “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environment in which it occurs.”* *1st International Conference of Learning Analytics & Knowledge, Banff, Alberta 2011
  6. 6. MOOCs (Massive Open Online Courses) § The term MOOCs has been first appeared in 2008 § The first xMOOC was the course by Stanford University in 2011.
  7. 7. 2. Research Methods § The search string used are “Learning Analytics” and “MOOCs” and “Massive Open Online Course” and “Learning Analytics - MOOCs”. § Search queries were conducted in selected databases between 2009-2019 dates. § As a result, as a part of this review, it was referenced a total of 50 articles.
  8. 8. Database Last 10 Years Publication Numbers Keywords TUBibliothek 7 Learning Analytics 6 MOOCs 2 Massive Open Online Course 2 Learning Analytics-MOOCs Semantic Scholar 647.937 Learning Analytics 29.204 MOOCs 131.640 Massive Open Online Course 7.146 Learning Analytics-MOOCs Web Of Science 6.300 Learning Analytics 2.714 MOOCs 2.447 Massive Open Online Course 202 Learning Analytics-MOOCs Scopus 10.113 Learning Analytics 3.467 MOOCs 3.028 Massive Open Online Course 356 Learning Analytics-MOOCs Google Scholar 450.000 Learning Analytics 31.700 MOOCs 295.000 Massive Open Online Course 12.900 Learning Analytics-MOOCs DergiPark 4.060 Learning Analytics 45 MOOCs 43.877 Massive Open Online Course 4.123 Learning Analytics-MOOCs
  9. 9. 3.Results of The Literature Review
  10. 10. Learning Analytics methods described by Khalil et al Data Mining Statistical and Mathematical Text Mining-Semantics- Linguistics Analysis Visulization Social Network Analysis Gamification Learning Analytics Methods
  11. 11. Data Categories/Typical Examples • Explore student’s learning ways • Supports to improve MOOCs environments • In the study conducted by Mukala et al, Using the data mining technique, it is seen that the student’s video surveillance have a direct impact on performance. DataMining Predictions Classification Assosiation Rule in Mining Clustering Fuzzy Logic
  12. 12. Data Categories/Typical Examples • can use to observe the relationship between participation rates and achievements. • Data such as online durations, number of being active, visited pages etc. • In the study carried out by Taraghi et al. has been determined the easiest and most difficult questions in different types and for result used Markov statistical analysis method. Statisticaland Mathematical • Average • Mean • Standard Deviotion • Markov Chain
  13. 13. Data Categories/Typical Examples • Discussion sections on forums and blogs • Tucker and Pursel worked on the effects of the texts produced by the students on the student performances and outputs. Text Mining-Sematics- Linguistics Analysis • Summarization • Categorization • Retrievals • Extract • Cluster https://pixabay.com/illustrations/text-mining-entity-extraction-1476780/
  14. 14. Data Categories/Typical Examples • used for doing proper learning, develop brainstorming and creativity. • Zhang and Yuan have worked on video recordings in MOOCs to explore the relationship among courses. Visulization • Ming Map • Concept Map • Cognigative Map • Radial Tree • Semantic Map • Rhizome • Visual Metaphor • Tree Structure • Argument Map • Social Map
  15. 15. Data Categories/Typical Examples • to understand and optimize the learning environment • Kellogg, Booth and Oliver use this method order to present a peer-assisted learning approach in MOOCs. Social Network Analysis Blockmodelling and Equivalence Analysis Signed Graphs and Structural Balance Analysis Dynimic Network Analysis • https://pixabay.com/illustrations/network-connections-communication-3537400/
  16. 16. Data Categories/Typical Examples Gamification • Visual Status • Social Engagament • Freedom of Choice • Freedom to Fail • Rapid feedback • Progress § increases student motivation, learning platform usage § participation in courses and makes learning entertaining § Vaibhav and Gupta have done Gamified and Non Gamified groups on MOOCs and analyzed the differences.
  17. 17. Benefits and Challanges Benefits: • Determining the target course • Improvement of the curriculum • Student learning outcomes, behaviors and processes • Individualized learning • Teacher performance improvement • Post-educational employment. Challenges: • The necessity of establishing strong connections with learning science • Difficulty in understanding and optimizing learning environments and MOOCs. • Focus on students’ point of view • Not to develop and implement a clear set of ethical rules
  18. 18. 4. Conclusion Learning Analytics is a new area and ever-developing concept. In addition to this, MOOCs with the huge data sources have an important role in applying learning analysis. The state-of-the-art methods of learning analytics in MOOC are described in this study. Data mining, statistics and mathematics, Text Mining, Semantics linguistics Analysis, visualization, Social network analysis and Gamification areas are implemented Learning Analytics in MOOCs allied with benefits and challenges.

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