Data Mining Techniques in Analyzing Process Data
Completion requirements
This paper notes that previous papers that explore how data methods can be used to analyze process data in log files of technology-enhanced assessments are limited in that they only explore the efficacy of one data mining technique under one specific scenario. This also demonstrates the usage of four often-used supervised learning techniques and two unsupervised methods fitted to one assessment data and discusses the pros and cons of each. For example, the authors note that regression trees may deal with noise well but are easily influenced by small changes. Can you differentiate between a confirmatory approach and an exploratory approach?
Results
The tuning and training results for the four supervised learning techniques are first reported and then the evaluation of their performance on the test datasets. Lastly, the results for the unsupervised learning methods are presented.