Enhancing data pipelines for forecasting student performance: integrating feature selection with cross-validation

Is Active Learning Enough? The Contributions of Misconception-Focused Instruction and Active-Learning Dosage on Student Learning of Evolution

Prior studies of active learning (AL) efficacy have typically lacked dosage designs (e.g., varying intensities rather than simple presence or absence) or specification of whether misconceptions were part of the instructional treatments. In this study, we examine the extent to which different doses of AL (approximately 10%, 15%, 20%, 36% of unit time), doses of misconception-focused instruction (MFI; approximately 0%, 8%, 11%, 13%), and their intersections affect evolution learning. A quantitative, quasiexperimental study (N > 1500 undergraduates) was conducted using a pretest, posttest, delayed posttest design with multiple validated measures of evolution understanding. The student background variables (e.g., binary sex, race or ethnicity), evolution acceptance, and prior coursework were controlled. The results of hierarchical linear and logistic models indicated that higher doses of AL and MFI were associated with significantly larger knowledge and abstract reasoning gains and misconception declines. MFI produced significant learning above and beyond AL. Explicit misconception treatments, coupled with AL, should be explored in more areas of life science education.

Optimization of Students’ Performance Prediction through an Iterative Model of Frustration Severity

Recent articles reported a massive increase in frustration among weak students due to the outbreak of COVID-19 and Massive Open Online Courses (MOOCs). These students need to be evaluated to detect possible psychological counseling and extra attention. On the one hand, the literature reports many optimization techniques focusing on existing students' performance prediction systems. On the other hand, psychological works provide insights into massive research findings focusing on various students' emotions, including frustration. However, the synchronization among these contributions is still a black box, which delays the mathematical modeling of students' frustration. Also, the literature is still limited in using insights of psychology and assumption-based datasets to provide an in-house iterative procedure for modeling students' frustration severity. This paper proposes an optimization technique called the iterative model of frustration severity (IMFS) to explore the black box. It analyzes students' performance via two modules. First, frustration is divided into four outer layers. Second, the students' performance outcome is split into 34 inner layers. The prediction results are iteratively optimized under the umbrella of frustration severity layers through the outer and inner iterations. During validation, the IMFS achieves promising results with various evaluation measures.