The use of recorded lecture videos (RLVs) in mathematics instruction continues to advance. Prior research at the post-secondary level has indicated a tendency for RLV use in mathematics to be negatively correlated with academic performance, although it is unclear whether this is because regular users are generally weaker mathematics students or because RLV use is somehow depressing student learning. Through the lens of cognitive engagement, a quasi-experimental pre- and post-test design study was conducted to investigate the latter possibility.
Cognitive engagement was operationalized using the Revised Two-Factor Study Process Questionnaire (R-SPQ-2F), which measures learning approaches on two major scales: surface and deep. In two mathematics courses at two universities, in Australia and the UK, participants were administered the questionnaire near the course start and finish. Overall findings were similar in both contexts: a reduction in live lecture attendance coupled with a dependence on RLVs was associated with an increase in surface approaches to learning.
This study has important implications for future pedagogical development and adds to the sense of urgency regarding research into best practices using RLVs in mathematics. 相似文献
L1 regularization and Lp regularization are proposed for processing recovered images based on compressed sensing (CS). L1 regularization can be solved as a convex optimization problem but is less sparse than Lp (0 < p < 1). Lp regularization is sparser than L1 regularization but is more difficult to solve. This paper proposes joint L1/Lp (0 < p < 1) regularization, which combines Lp regularization and L1 regularization. This joint regularization is applied to recover video of remote sensing based on CS. Joint regularization is sparser than L1 regularization but is as easy to solve as L1 regularization. A linearized Bregman reweighted iteration algorithm is proposed to solve the joint L1/Lp regularization problem. The performance and capabilities of the linearized Bregman algorithm and linearized Bregman reweighted algorithm for solving the joint L1/Lp regularization model are analyzed and compared through numerical simulations. 相似文献