Educational Data Mining and Learning Analytics in Programming: Literature Review and Case Studies

By Petri Ihantola, Arto Vihavainen, A. Ahadi, M. Butler, J. Börstler, S. Edwards, Essi Isohanni, A. Korhonen, Andrew Petersen, Kelly Rivers and 6 more. ITiCSE-WGR, 2015

343 citations recorded by the source corpus.

Educational data mining and learning analytics promise better understanding of student behavior and knowledge, as well as new information on the tacit factors that contribute to student actions. This knowledge can be used to inform decisions related to course and tool design and pedagogy, and to further engage students and guide those at risk of failure. This working group report provides an overview of the body of knowledge regarding the use of educational data mining and learning analytics focused on the teaching and learning of programming. In a literature survey on mining students' programming processes for 2005-2015, we observe a significant increase in work related to the field. However, the majority of the studies focus on simplistic metric analysis and are conducted within a single institution and a single course. This indicates the existence of further avenues of research and a critical need for validation and replication to better understand the various contributing factors and the reasons why certain results occur. We introduce a novel taxonomy to analyse replicating studies and discuss the importance of replicating and reproducing previous work. We describe what is the state of the art in collecting and sharing programming data. To better understand the challenges involved in replicating or reproducing existing studies, we report our experiences from three case studies using programming data. Finally, we present a discussion of future directions for the education and research community.

Subfields: Computer Science Applications, Software

View Paper: https://www.semanticscholar.org/paper/ef9c92568b00426cca55f81e12d6b5b63c5df2b5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Science Applications

1. Juho Leinonen
2. Nea Pirttinen
3. Arto Hellas
4. Paul Denny
5. Sami Sarsa

In Software

1. Arto Hellas
2. Juho Leinonen
3. Paul Denny

Citations in the corpus

• Automated Questionnaires About Students’ JavaScript Programs: Towards Gauging Novice Programming Processes (2023, background): “A working group report [10] provides a broader literature review of research related to data in learning programming—including data from programming processes.”
• Crowdsourcing programming assignments with CrowdSorcerer (2018, methodology): “Such data has been used, for example, to improve learning materials [14], create predictive models that can be used to identify at-risk students [12], and to study students’ behavior and learning [12].”