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Advances in Learning Analytics (LA) &
Educational Data Mining (EDM)
Mehrnoosh Vahdat, Luca Oneto, Alessandro Ghio,
Davide Anguita, Mathias Funk, and Matthias Rauterberg
April 22-24, 2014
European Symposium on Artificial Neural Networks,Computational Intelligence and Machine Learning
• Development of public data repositories (PSLC DataShop)
• Data from MOOCs
• Advantages of open data resources: • Used as benchmarks to develop new algorithms [5]
• Availability of data resources is a motivation for research in the field [6]
Outline
• Introduction
• What is LA/EDM?
• LA/EDM Goals
• Similarities and Differences of LA/EDM
• EDM/ LA Process
• Stakeholders
• Challenges
• Data and Methods of LA/ EDM
• Applications of LA/EDM
What is LA/ EDM?• LA is:
• a multi-disciplinary field involving machine learning, artificial intelligence, information retrieval, statistics, and visualization [1]
• the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs [7]
• EDM is concerned with:• developing, researching, and applying computerized methods to detect patterns in
large collections of educational data that would otherwise be hard or impossible to analyze due to the enormous volume of data within which they exist [8]
• They are complementary• LA: Holistic framework, understands systems in full complexity
• EDM: Reductionist viewpoint, seeks new patterns, modifies algorithms [12]
LA/ EDM Process
In LA/ EDM process, data is collected and analyzed, and after post processing, feedback and interventions are made in order to optimize learning (based on [1, 13])
Stakeholders
• Educators: • To design, decide, plan, building the systems
• To know the needs of students, mistakes
• For real-time insight into the performance,
help at-risk students
• Learners/ Users: • To get recommendations about activities/resources, learning tasks/ path
• To receive information about their progress/performance compared to peers
• To be motivated and encouraged
• Administrators: • To improve system, adapt to needs
• Developing decision supports and recommendation engines
• Standardization of methods and data
• Developing context-adapted models
• Integration with the e-learning system
• Specific data mining techniques
• Advancing the anonymization of data [1, 8, 9, 11]
Special Session Contributions • Adaptive structure metrics for automated feedback provision in java
programming. B. Paassen, B. Mokbel, and B. Hammer
• Human algorithmic stability and human rademacher complexity. M. Vahdat, L. Oneto, A. Ghio, D. Anguita, M. Funk, and M. Rauterberg
• High-school dropout prediction using machine learning: A danish large-scale study.
N.B. Sara, R. Halland, C. Igel, and S. Alstrup
• The prediction of learning performance using features of note taking activities.
M. Nakayama, K. Mutsuura, and H. Yamamoto
• Enhancing learning at work. how to combine theoretical and data-driven approaches, and multiple levels of data?
V. Kalakoski, H. Ratilainen, and L. Drupsteen
• Weighted clustering of sparse educational data. M. Saarela and T. Kärkkäinen
References• [1] M. A. Chatti, A. L. Dyckhoff, U. Schroeder, and H. Thüs. A reference model for learning analytics. International Journal
of Technology Enhanced Learning, 4(5):318–331, 2012.
• [2] W. Greller and H. Drachsler. Translating learning into numbers: A generic framework for learning analytics. Educational Technology & Society, 13(3):42–57, 2012.
• [3] G. Siemens. Learning analytics: envisioning a research discipline and a domain of practice. In International Conference on Learning Analytics and Knowledge, 2012.
• [4] N. Bousbia and I. Belamri. Which contribution does edm provide to computer–based learning environments? In Educational Data Mining, 2014.
• [5] R. Baker and K. Yacef. The state of educational data mining in 2009: A review and future visions. JEDM-Journal of Educational Data Mining, 1(1):3–17, 2009.
• [6] K. Verbert, N. Manouselis, H. Drachsler, and E. Duval. Dataset-driven research to support learning and knowledge analytics. Educational Technology & Society, 15(3):133–148, 2012.
• [7] http://www.solaresearch.org/about
• [8] C. Romero, S. Ventura, M. Pechenizkiy, and Ryan S. Baker. Handbook of educational data mining. Data Mining and Knowledge Discovery Series. Boca Raton, FL: Chapman and Hall/CRC Press, 2010.
• [9] M. Bienkowski, M. Feng, and B. Means. Enhancing teaching and learning through educational data mining and learning analytics: An issue brief. In US Department of Education, Office of Educational Technology, 2012.
• [10] W. He. Examining students’ online interaction in a live video streaming environment using data mining and text mining. Computers in Human Behavior, 29(1):90–102, 2013.
• [11] G. Siemens and R. Baker. Learning analytics and educational data mining: towards communication and collaboration. In international conference on learning analytics and knowledge, 2012.
References• [12] Z. Papamitsiou and A. Economides. Learning analytics and educational data mining in practice: A systematic
literature review of empirical evidence. Journal of Educational Technology & Society, 17(4):49–64, 2014.
• [13] D. Clow. The learning analytics cycle: Closing the loop effectively. In International Conference on Learning Analytics and Knowledge, 2012.
• [14] C. Romero and S. Ventura. Educational data mining: A survey from 1995 to 2005. Expert systems with applications, 33(1):135–146, 2007.
• [15] G. Siemens and P. Long. Penetrating the fog: Analytics in learning and education. Educause Review, 46(5):30–32, 2011.
• [16] Drachsler, Hendrik, and Wolfgang Greller. "The pulse of learning analytics understandings and expectations from the stakeholders." Proceedings of the 2nd international conference on learning analytics and knowledge. ACM, 2012.
• [17] A. Del Blanco, A. Serrano, M. Freire, I. Mart´ınez-Ortiz, and B. Fernández-Manjón. E-learning standards and learning analytics. can data collection be improved by using standard data models? In Global Engineering Education Conference, 2013.
• [18] K. Gyllstrom. Enriching personal information management with document interaction histories. PhD thesis, The University of North Carolina at Chapel Hill, 2009.
• [19] R. Ferguson. Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5):304–317, 2012.
• [20] A. Pena-Ayala. Educational data mining: A survey and a data mining-based analysis of recent works. Expert systems with applications, 41(4):1432–1462, 2014.
• [21] K. E. Arnold and M. D. Pistilli. Course signals at purdue: Using learning analytics to increase student success. In International Conference on Learning Analytics and Knowledge, 2012.
Advances in Learning Analytics (LA) & Educational Data Mining (EDM)Was presented by Mehrnoosh VahdatAt European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
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