Identifying priority antecedents of educational data mining acceptance using importance-performance matrix analysis

Previous studies on educational data mining (EDM) acceptance were focused on antecedents that were adopted from various models and theories. However, the ways in which such antecedents became the most important tools for educational improvement have not been researched in detail. This study aims to identify the priority antecedents of EDM acceptance, particularly among undergraduate students since they are the most affected by this technology. Therefore, six antecedents with 11 variables have been formulated based on positive and negative readiness acquired from the technology readiness index (TRI). Meanwhile, cognition, emotion, internal control belief, and external control belief were obtained from the technology acceptance model 3 (TAM3). The Importance-Performance Matrix Analysis (IPMA) was used to identify priority antecedents of EDM acceptance, which was run using the SmartPLS 3.0 software. The findings revealed that perceived usefulness (PU) is the most important antecedent, followed by perceived ease of use (PEOU), and optimism (OPT). This study contributes to the literature by offering new insights on the field of EDM and extending existing knowledge on how cognition, positive readiness, negative readiness, emotion, internal control belief, and external control belief were combined for identifying priority antecedents of EDM acceptance.