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Learning Analytics: Trends and Issues of the
Empirical Research of the Years 2011-2014
Nic. Nistor, Michael Derntl, & Ralf...
1. Introduction
Tasks and methods of Learning Analytics (LA)
(Baker & Siemens, 2015)
•  predicting learning behaviour and ...
2. Purpose
Overview of the empirical LA research
2011-2014
•  educational setting
•  data sources
•  theoretical framework...
3. Methodology
Methodology
•  Analysed body of literature: 480 conference papers
–  298 papers from EC-TEL 2011-2014
–  18...
3. Methodology
Methodology
•  dynamic topic model using the topic modelling toolkit D-
VITA (http://monet.informatik.rwth-...
4. Findings
Educational setting
•  University/college: 12 cases, [8–10, 12, 13, 15,
17–19, 21, 23, 25]
•  School: 6 cases,...
4. Findings
Data sources – from isolated (scalable)
cases to Big Data
•  Learning management system log data: 11
cases, [8...
4. Findings
Theoretical framework from educational
perspective
•  No Specific Educational Theoretical Framework
Acknowledg...
4. Findings
Purpose of data processing
•  Learning Trajectory Visualization: 7 cases, [9, 10,
12, 13, 21, 23, 25]
•  Predi...
4. Findings
Associated computational methods
•  Prediction: 7 cases [10, 15, 16, 18, 21, 22, 24]
•  Structure Discovery: 3...
6. Discussion
Summary of findings
•  Mainstream:
–  examine log data to visualize learning trajectories and
predict the su...
7. Consequences
Recommendation
•  Educational and psychological theories urgently
needed for in-depth understanding of
edu...
Thank you for your attention!
nic.nistor@lmu.de
Nistor, Derntl, & Klamma, EC-TEL2015
References
LAK 2014 Papers
•  6. Pistilli MD, Willis J, Koch D et al. (eds) (2014) Proceedings of Learning Analytics and K...
References
EC-TEL 2014 Papers
•  7. Rensing C, Freitas S de, Ley T et al. (eds) (2014) Open Learning and Teaching in Educa...
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Learning Analytics: Trends and Issues of the Empirical Research of the Years 2011-2014

NIc. Nistor, Michael Derntl, Ralf Klamma
EC-TEL 2015, Toledo, Spain

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Learning Analytics: Trends and Issues of the Empirical Research of the Years 2011-2014

  1. 1. Learning Analytics: Trends and Issues of the Empirical Research of the Years 2011-2014 Nic. Nistor, Michael Derntl, & Ralf Klamma EC-TEL 2015 Toledo, Spain
  2. 2. 1. Introduction Tasks and methods of Learning Analytics (LA) (Baker & Siemens, 2015) •  predicting learning behaviour and output •  structure discovery: clustering (learner types), social network analysis •  relationship mining •  distillation of data for human judgement: monitoring, visualizing •  discovery with models •  tool development for LA Ø  Open issue… Nistor, Derntl, & Klamma, EC-TEL2015
  3. 3. 2. Purpose Overview of the empirical LA research 2011-2014 •  educational setting •  data sources •  theoretical framework from educational perspective •  purpose of data processing •  associated computational methods Nistor, Derntl, & Klamma, EC-TEL2015
  4. 4. 3. Methodology Methodology •  Analysed body of literature: 480 conference papers –  298 papers from EC-TEL 2011-2014 –  182 papers from LAK 2011-2014 •  197 papers (71 EC-TEL, 126 LAK) were considered relevant Nistor, Derntl, & Klamma, EC-TEL2015
  5. 5. 3. Methodology Methodology •  dynamic topic model using the topic modelling toolkit D- VITA (http://monet.informatik.rwth-aachen.de/DVita?id=3001) •  Final manual analysis of 19 most central papers (12 LAK 2014, 7 EC-TEL 2014) Nistor, Derntl, & Klamma, EC-TEL2015
  6. 6. 4. Findings Educational setting •  University/college: 12 cases, [8–10, 12, 13, 15, 17–19, 21, 23, 25] •  School: 6 cases, [11, 14, 16, 20, 22, 24] •  Informal Settings: Total: 1 case, [26] Nistor, Derntl, & Klamma, EC-TEL2015
  7. 7. 4. Findings Data sources – from isolated (scalable) cases to Big Data •  Learning management system log data: 11 cases, [8, 9, 11, 13, 14, 18–20, 23–25] •  Learner-generated data: 4 cases, [10, 12, 21, 26] •  Additionally collected data: 3 cases, [16, 17, 22] •  Available academic administration data: 1 case, [15] Nistor, Derntl, & Klamma, EC-TEL2015
  8. 8. 4. Findings Theoretical framework from educational perspective •  No Specific Educational Theoretical Framework Acknowledged: 7 cases, [8, 13, 15, 18, 23–25] •  Linguistic/Logic Approach: 3 cases, [10, 21, 26] •  Situated Learning/Community of Practice: 2 cases, [9, 16] •  Vygotsky’s Zone of Proximal Development: 2 cases, [14, 22] •  Various Approaches: self-regulated learning [12], adaptive learning environments [11], peer assessment [19], autism [20], or the instructional design concept of learning orchestration [17] Nistor, Derntl, & Klamma, EC-TEL2015
  9. 9. 4. Findings Purpose of data processing •  Learning Trajectory Visualization: 7 cases, [9, 10, 12, 13, 21, 23, 25] •  Predicting Learning Success or Failure/Dropout: 5 cases, [8, 14, 15, 18, 22] •  Diagnosis: 3 cases, [17, 20, 24] •  Assessment: 2 cases, [16, 19] •  Intervention (e.g., automatically adapting or recommending learning material): 2 cases, [11, 26] Nistor, Derntl, & Klamma, EC-TEL2015
  10. 10. 4. Findings Associated computational methods •  Prediction: 7 cases [10, 15, 16, 18, 21, 22, 24] •  Structure Discovery: 3 cases, clustering [8, 26] and social network analysis [13] •  Relationship Mining: 6 cases, correlation mining [12, 17, 19], association rule mining [11] and others with mixed approaches [20, 25] •  Visualization: 3 cases, [9, 14, 23] Nistor, Derntl, & Klamma, EC-TEL2015
  11. 11. 6. Discussion Summary of findings •  Mainstream: –  examine log data to visualize learning trajectories and predict the success or failure of university students •  Innovative studies: –  informal learning in online communities –  video/audio records –  automated student learning assessment –  error/misconception diagnosis Nistor, Derntl, & Klamma, EC-TEL2015
  12. 12. 7. Consequences Recommendation •  Educational and psychological theories urgently needed for in-depth understanding of educational phenomena, and for significant progress of upcoming LA research Nistor, Derntl, & Klamma, EC-TEL2015
  13. 13. Thank you for your attention! nic.nistor@lmu.de Nistor, Derntl, & Klamma, EC-TEL2015
  14. 14. References LAK 2014 Papers •  6. Pistilli MD, Willis J, Koch D et al. (eds) (2014) Proceedings of Learning Analytics and Knowledge Conference 2014, LAK ’14. ACM, New York •  8. Bogarín A, Romero C, Cerezo R et al. (2014) Clustering for improving educational process mining. In [6], pp 11–15 •  9. Clow D (2014) Data wranglers: human interpreters to help close the feedback loop. In [6], pp 49–53 •  10. Coopey E, Shapiro RB, Danahy E (2014) Collaborative spatial classification. In [6], pp 138–142 •  11. Fancsali SE, Ritter S (2014) Context personalization, preferences, and performance in an intelligent tutoring system for middle school mathematics. In [6], pp 73–77 •  12. Gasevic D, Mirriahi N, Dawson S (2014) Analytics of the effects of video use and instruction to support reflective learning. In [6], pp 123–132 •  13. Hecking T, Ziebarth S, Hoppe HU (2014) Analysis of dynamic resource access patterns in a blended learning course. In [6], pp 173–182 •  14. Mendiburo M, Sulcer B, Hasselbring TS (2014) Interaction design for improved analytics. In [6], pp 78–82 •  15. Nam S, Lonn S, Brown T et al. (2014) Customized course advising: investigating engineering student success with incoming profiles and patterns of concurrent course enrollment. In [6], pp 16–25 •  16. Okada M, Tada M (2014) Formative assessment method of real-world learning by integrating heterogeneous elements of behavior, knowledge, and the environment. In [6], pp 1–10 •  17. Raca M, Tormey R, Dillenbourg P (2014) Sleepers' lag - study on motion and attention. In [6], pp 36–43 •  18. Santos JL, Klerkx J, Duval E et al. (2014) Success, activity and drop-outs in MOOCs an exploratory study on the UNED COMA courses. In [6], pp 98–102 •  19. Vozniuk A, Holzer A, Gillet D (2014) Peer assessment based on ratings in a social media course. In [6], pp 133–137 Nistor, Derntl, & Klamma, EC-TEL2015
  15. 15. References EC-TEL 2014 Papers •  7. Rensing C, Freitas S de, Ley T et al. (eds) (2014) Open Learning and Teaching in Educational Communities: 9th European Conference on Technology Enhanced Learning, EC-TEL 2014, Graz, Austria, September 16-19, 2014, Proceedings. Lecture Notes in Computer Science, vol 8719. Springer, Berlin •  20. Cabielles-Hernández D, Pérez Pérez JR, Paule-Ruiz MP et al. (2014) dmTEA: Mobile Learning to Aid in the Diagnosis of Autism Spectrum Disorders. In [7], pp 29–41 •  21. González López S, López-López A (2014) Analysis of Concept Sequencing in Student Drafts. In [7], pp 422–427 •  22. Janning R, Schatten C, Schmidt-Thieme L (2014) Feature Analysis for Affect Recognition Supporting Task Sequencing in Adaptive Intelligent Tutoring Systems. In [7], pp 179–192 •  23. Loboda TD, Guerra J, Hosseini R et al. (2014) Mastery Grids: An Open Source Social Educational Progress Visualization. In [7], pp 235–248 •  24. McTavish TS, Larusson JA (2014) Labeling Mathematical Errors to Reveal Cognitive States. In [7], pp 446– 451 •  25. Vahdat M, Oneto L, Ghio A et al. (2014) A Learning Analytics Methodology to Profile Students Behavior and Explore Interactions with a Digital Electronics Simulator. In [7], pp 596–597 •  26. Niemann K, Wolpers M (2014) Usage-Based Clustering of Learning Resources to Improve Recommendations. In [7], pp 317–330 Nistor, Derntl, & Klamma, EC-TEL2015

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