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1. Introduction
Educational data mining (EDM) is an emerging research area focused on discovering patterns from educational data to help understand the relations between learners and educational settings. However, most educational data mining techniques focus on predicting learning performance based on learners’ profiles, rather than identifying their characteristics to evaluate their learning performance. In particular, the characteristics of learners with low learning performance are necessary to initiate early intervention for learners who need teaching assistance.
This study considers association-based classification patterns, which are used to identify the associations between cause and effect to establish students’ learning performance profiles. Furthermore, we propose a measure (OddsRatio) to determine valuable patterns from each cluster of instances. For example, (Pattern X1) → (Bad) means that students belonging to the learning outcome (Bad) group have the characteristics: (Pattern X1). We also identify another pattern: (Pattern X2) → (Good). Identifying the difference (ΔX) between patterns (X1 and X2) that leads to different results is the main purpose of this study. The knowledge in this example reveals the association causes for the effect. Assume that we have two patterns, Pattern X1 = {(Paid = no) → (Bad); support = 0.92; count = 293; OddsRatio = 1.08} and Pattern X2 = {(Higher = yes, Paid = no) → (Good); support = 0.94; count = 289; OddsRatio = 1.13}; therefore, the difference (ΔX) between two patterns (X2 and X1) is {(Higher = yes)}.
The above example indicates that difference pattern (ΔX), {(Higher = yes)}, is a change pattern for instances (students) with pattern X1 = {(paid = no) in cluster learning performance (Bad) who move to cluster learning performance (Good). The knowledge, pattern {(Higher = yes)}, in the above example reveals the association causes for the effect, cluster learning performance (Bad) moving to cluster learning performance (Good). However, no studies in the education data mining field, to our knowledge, have addressed the important issue of change patterns, difference (ΔX), identification in association-based classification patterns.
It is important for educational institutions to have approximate prior knowledge of students to predict their performance in future academics. To address these problems, we propose a framework for identifying learning patterns and predicting learning...