Home / MRES.B.02.08 E-learning: Mining, Analytics and Visualization of Educational Data

MRES.B.02.08 E-learning: Mining, Analytics and Visualization of Educational Data

 

 

 

 

 

 

This course module focuses on E-Learning technologies as implemented in modern e-learning platforms that support synchronous and/or asynchronous education events and activities. The core content of the module is Educational Data Mining (EDM) a topic that covers the collection, retrieval, analysis and visualization of educational data produced in digital form. Such data is automatically collected by an e-learning platform (an LMS or a VLE, such as moodle) during the interaction of learners with the platform and the learning content as well as the collaboration of learners who work in teams over a platform. Educational data is subsequently analyzed in order to answer specific research questions that aim to provide feedback to learners, instructors and decision-making parties in Education, in an attempt to improve the learning outcomes as well as the learning experience. The later field is known as Learning Analytics (LA). Data analysis is performed by artificial intelligence / machine learning algorithms, methods and tools. Data visualization is performed using modern relevant tools and visualization environments.

 

Syllabus

  1. Introduction to Big Data, Data Mining and Educational Data Mining
  2. Data Mining Nomenclature – EDM or LA or both?
  3. Overview of current research and open questions
  4. Types of problems EDM addresses and the corresponding Machine Learning methods employed
  5. Classification, Prediction, Clustering: worked examples and case studies
  6. From Data Visualization to Visual Analytics: current state, gaps and potentials

Upon successful completion of the course, students are expected to be able to:

  1. Define EDM and LA and differentiate the two terms
  2. Describe the major types of educational data, their value and the means and processes for data collection
  3. State the major problems the relevant research seeks to solve and the major research questions that are open to date
  4. Implement major Machine Learning algorithms (ANN-DNN, SVM, Decision Trees, Bayesian etc.)
  5. Recognize as such and solve a classification problem using appropriate Machine Learning algorithms and tools
  6. Recognize as such and solve a prediction problem using appropriate Machine Learning algorithms and tools
  7. Recognize as such and solve a clustering problem using appropriate Machine Learning algorithms and tools
  8. Comparatively evaluate major data visualization tools and select the appropriate for the problem at hand
  9. Use data visualization tools to visualize educational data for the learner and/or the instructor.

An undergraduate course on Digital Signal Processing and/or Pattern Recognition would be useful.

Student evaluation comes from

  • An EDM / LA project (50% of the final grade) – individual or teamwork
  • A visualization project (50% of the final grade) – individual or teamwork
  • Baker, R. S. J.d., & Inventado, P. S. (2014). Educational data mining and learning analytics. In J. A. Larusson & B. White (Eds.), Learning analytics: From research to practice. Berlin, Germany: Springer.
  • Baker, R. S. (2015). Big data and education (2nd ed.). New York, NY: Teachers College, Columbia University.
  • D’Mello, S. (2017). Emotional learning analytics. In Handbook of learning analytics (p. 115). New York, NY: SOLAR
  • Lang, C., Siemens, G., Wise, A., & Gasevic, D. (2017). Handbook of learning analytics. SOLAR, Society for Learning Analytics and Research. New York, NY: SOLAR.
  • Leitner, P., Ebner, M., & Ebner, M. (2019). Learning analytics challenges to overcome in higher education institutions. In Utilizing learning analytics to support study success (pp. 91–104). Cham, Switzerland: Springer
  • Romero, C., & Ventura, S. (2006). Data mining in E-learning. Southampton, England: Wit-Press
  • Romero, C., Ventura, S., Pechenizky, M., & Baker, R. (2010). Handbook of educational data mining. Data Mining and Knowledge Discovery Series. Boca Raton, FL: Editorial Chapman and Hall/CRC Press, Taylor & Francis Group.

RESEARCH ARTICLES

  • P. Macfadyen and S. Dawson, “Mining LMS data to develop an ‘‘early warning system’’ for educators: A proof of concept,” Computers and Education, vol. 54, no. 2, pp. 588–599, 2010, doi: 10.1016/j.compedu.2009.09.008.
  • Siemens and R. S. Baker, “Learning analytics and educational data mining: Towards communication and collaboration,” In Proc. 2nd International Conference on Learning Analytics and Knowledge (LAK’12), Vancouver, BC, Canada, 2012, pp. 252–254
  • Romero and S. Ventura, “Data mining in education,” WIREs Data Mining and Knowledge Discovery, vol. 3, pp. 12-27, 2013, doi: 10.1002/widm.1075.
  • Peña-Ayala, “Educational data mining: A survey and a data mining-based analysis of recent works,” Expert Systems with Applications, vol. 41 (4 part 1), pp. 1432-1462, 2014.
  • Aldowah, H. Al-Samarraie, and W. M. Fauzy, “Educational data mining and learning analytics for 21st century higher education: A review and synthesis,” Telematics and Informatics, vol. 37, pp. 13-49, 2019.
  • H. Bin Roslan and C. J. Chen, “Educational Data Mining for Student Performance Prediction: A Systematic Literature Review (2015-2021),” International Journal of Emerging Technologies in Learning (iJET), vol. 17, no. 5, pp. 147–179, 2022, https://doi.org/10.3991/ijet.v17i05.27685.
  • Charitopoulos, M. Rangoussi, and D. Koulouriotis, “On the Use of Soft Computing Methods in Educational Data Mining and Learning Analytics Research: a Review of Years 2010–2018,” International Journal of Artificial Intelligence in Education, vol. 30, no. 3, pp. 371-430, 2020, doi:10.1007/s40593-020-00200-8.
  • Roll and R. L. Wylie, “Evolution and revolution in artificial intelligence in education”, International Journal of Artificial Intelligence in Education, vol. 26, no. 2, pp. 582–599, 2016.
  • Imran, S. Latif, D. Mehmood, and M. S. Shah, “Student Academic Performance Prediction using Supervised Learning Techniques,” International Journal of Emerging Technologies in Learning (iJET), vol. 14, no. 14, pp. 92–104, 2019, https://doi.org/10.3991/ijet.v14i14.10310.
  • Polyzou and G. Karypis, “Feature Extraction for Next-Term Prediction of Poor Student Performance,” IEEE Trans. on Learning Technologies, vol. 12, pp. 237–248, 2019.
  • T. Tempelaar, B. Rienties, and B. Giesbers, “In search for the most informative data for feedback generation: Learning analytics in a data-rich context,” Computers in Human Behavior, vol. 47, pp. 157–167, 2015.
  • Jokhan, B. Sharma, and S. Singh, “Early warning system as a predictor for student performance in higher education blended courses,” Studies in Higher Education, vol. 44, no. 11, pp. 1900-1911, 2019, doi:10.1080/03075079.2018.1466872.

TOOLS

WEBSITES

instructor(s) : Maria RANGOUSSI & Katerina ZACHARIADOU

 

MRES.B.02.08 E-learning: Mining, Analytics and Visualization of Educational Data