Using learning analytics to assess students' behavior in open-ended programming tasks

By Paulo Blikstein. International Conference on Learning Analytics and Knowledge, 2011

319 citations recorded by the source corpus.

There is great interest in assessing student learning in unscripted, open-ended environments, but students' work can evolve in ways that are too subtle or too complex to be detected by the human eye. In this paper, I describe an automated technique to assess, analyze and visualize students learning computer programming. I logged hundreds of snapshots of students' code during a programming assignment, and I employ different quantitative techniques to extract students' behaviors and categorize them in terms of programming experience. First I review the literature on educational data mining, learning analytics, computer vision applied to assessment, and emotion detection, discuss the relevance of the work, and describe one case study with a group undergraduate engineering students

Subfields: Artificial Intelligence, Computer Science Applications

View Paper: https://www.semanticscholar.org/paper/81201080b2adc713585448e4bf1cb98503224ca4

How researchers see this paper

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

In Artificial Intelligence

1. Marie A. Bienkowski
2. Anastasios A. Economides
3. Z. Papamitsiou
4. Rebecca Ferguson
5. Mingyu Feng
6. B. Means

In Computer Science Applications

1. Ulrik Schroeder
2. Marie A. Bienkowski
3. Mohamed Amine Chatti
4. Anastasios A. Economides
5. Z. Papamitsiou
6. Rebecca Ferguson
7. Mingyu Feng
8. Anna Lea Dyckhoff

Citations in the corpus

• Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics: An Issue Brief (2012, methodology): “For instance, Blikstein (2011) has presented an automated technique and a case study to assess, analyze, and visualize behaviors of students learning computer programming.”
• The AI Revolution in Education: Will AI Replace or Assist Teachers in Higher Education? (2023, unknown): “And not just that, in a study by Blikstein (2016), he found that AI-supported classrooms yielded higher engagement levels and greater student achievement compared to traditional classrooms.”
• Learning analytics: drivers, developments and challenges (2012, background): “Building up an holistic picture of student progress and taking sentiment into account in order to enable ‘computer-based systems to interact with students in emotionally supportive ways’ is now seen as a real possibility (Blikstein, 2011, p.110).”
• A reference model for learning analytics (2012, background): “, to monitor the activity on a site and the usefulness of a reputation system (Clow and Makriyanni, 2011), to inspect students' behavior and learning in project-based, unscripted constructionist learning environments (Blikstein, 2011), or to analyze of portal usage in relation to specific training e”
• Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence (2014, background): “Reveal critical moments and patterns of learning.”