Tag Archives: Educational data mining

Type: Evidence | Proposition: B: Teaching | Polarity: | Sector: | Country:

ABSTRACT
In this paper, we incorporate scaffolding and change of tutor context within the Bayesian Knowledge Tracing (BKT) framework to track students’ developing inquiry skills. These skills are demonstrated as students experiment within interactive simulations for two science topics. Our aim is twofold. First, we desire to improve the models’ predictive performance by adding these factors. Second, we aim to interpret these extended models to reveal if our scaffolding approach is effective, and if inquiry skills transfer across the topics. We found that incorporating scaffolding yielded better predictions of individual students’ performance over the classic BKT model. By interpreting our models, we found that scaffolding appears to be effective at helping students acquire these skills, and that the skills transfer
across topics.

COMMENT
This paper reports research using Bayesian Knowledge Tracing to predict student performance in inquiry process skills across science topics (particularly data collection).  The main focus is on the accuracy of the prediction but they also use it to evaluate the effectiveness of scaffolding offered.  This could be used positive evidence for the LACE hypothesis B – improving teaching.

 

Citation: Sao Pedro, M., Baker, R.S.J.D. and Gobert, J, Incorporating Scaffolding and Tutor Context into Bayesian Knowledge Tracing to Predict Inquiry Skill Acquisition, S. D'Mello, R. Calvo, & A. Olney (Eds.). Proc Educational Data Mining (EDM) 2013 | Url: https://sites.google.com/a/iis.memphis.edu/edm-2013-conference/proceeding

Type: Evidence | Proposition: A: Learning | Polarity: | Sector: | Country:

LA/EDM research results indicate that data integration from multiple sources can improve the accuracy of a learner profile and subsequent adaptation and personalization of content. Exploration of students’ behavior within educational contexts that support multimodality and mobility could lead to shaping a holistic picture of how, when and where learning occurs.

The paper reports an examination of the literature on experimental case studies conducted in the domain from 2008 to 2013. Although the search terms identified 209 mature pieces of research work, the inclusion criteria limited the key studies to 40. The research questions, methodology and findings of these published papers were analysed and categorized. The authors used non-statistical methods to evaluate and interpret findings of the collected studies. Their results highlighted four distinct major directions for learning analytics / educational data mining empirical research. The paper also discusses the value added by this research

Citation: Papamitsiou, Z., & Economides, A. A. (2014). Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence. Educational Technology & Society, 17(4), 49