An intelligent diagnostic framework: A scaffolding tool to resolve academic reading problems of Thai first-year university students

To accommodate teaching an English class with varied language abilities, an intelligent diagnostic framework (IDF) employing the twostep clustering (TSC) of data mining technique was proposed. A tailormade diagnostic test on the 10 underlying academic reading skills was constructed. Each skill was measured by four test items using a pass criterion of 75% (≥ 3 out of 4). The TSC was performed on the skill scores and ten personal attributes of 297 first-year university students. The precluster step generated three subclusters. Further analysis (N= 221) created a predictive solution of five clusters with 95.5% accuracy. A final analysis using Pearson's correlation revealed four groups of positive relationships. Lead users from each type were then assigned self-tutoring lessons to learn for two weeks. The results revealed that 56% of lead users had equal or higher scores and 68% of them passed an equal or higher number of skills than in the pretest. Students' types disclosed by the TSC were thus able to predict and the IDF was able to diagnose and scaffold most of the students in academic reading skills. Because the IDF was not so powerful for lower-proficiency students, future research should focus more on those students.