Sustainable Math Education of Female Students during a Pandemic: Online versus Face-to-Face Instruction

Abstract

The present study was driven by the assumption that a key feature of sustainable education is its ability to preserve standards of quality even amid unforeseen, potentially disruptive events. It asked whether students’ academic success in math general education courses differed between synchronous online (during the COVID-19 pandemic) and face-to-face (before the pandemic), under the ancillary assumption that computational competency, a pillar of sustainable education, shapes enduring success in a variety of professional fields. As the early identification of at-risk students and ensuing remedial interventions can bring about academic success, the study also investigated the predictive validity of students’ initial performance in online and face-to-face math courses. Two general education courses (introductory calculus and statistics), taught by the same instructor, were selected. Class grades did not differ between instructional modes, thereby providing no evidence for the widespread concern that the switch to the online mode had damaged learning. Yet, during the semester, test and homework performance were differentially sensitive to modes of instruction. Furthermore, both test and homework performance during the first half of the semester predicted class grades in online courses, whereas only test performance predicted class grades in face-to-face courses. These results suggest that sustainable math education in times of crisis is feasible and that educators’ consideration of the differential predictive value of test and homework performance may aid its attainment.

The effectiveness of web-based Mathematics instruction (WBMI) on K-16 students’ mathematics learning: a meta-analytic research

“…(e.g., Jaggers, 2014 ; Smith & Ferguson, 2005 ; Summers, Waigandt; Whittaker, 2005 ; Zavarella & Ignash, 2009 ). On the other hand, recent studies have indicated that students have significantly higher academic performance in STEM-related online courses compared to face-to-face courses (AbdelSalam, Pilotti, & El-Moussa, 2021 ; Gonzalez et al, 2020 ; Iglesias-Pradas et al, 2021 ). The reason for this situation is that the COVID-19 pandemic has improved students’ digital skills and changed their learning strategies from discontinuous habits to continuous habits (Gonzalez et al, 2020 ).…”

“…The COVID 19 pandemic has also ensured that students do not become unfamiliar with online courses and can easily adapt to these courses. Therefore, it is argued that online learning environments can be used as a crucial tool to improve STEM-related performance in the post-pandemic world (AbdelSalam et al, 2021 ).…”

Measuring the Impact of the Pandemic on Female and Male Students’ Learning in a Society in Transition: A Must for Sustainable Education

“…During the pandemic, the unending debate regarding the quality of online learning relative to face-to-face learning has escalated in intensity and reach [18]. The term "emergency remote teaching" has been used to underscore that the sudden change to online instruction, experienced by students mostly accustomed to face-to-face instruction, was not an attempt "to re-create a robust educational ecosystem but rather to provide temporary access to instruction and instructional supports in a manner that is quick to set up and is reliably available during an emergency or crisis" [19].…”

“…The study asks whether there are differences in enrollment and performance (as measured by pass/fail grades) between face-to-face courses taught by the same instructors before and during the pandemic. As the study takes place in a society in transition from a patriarchal order based on tribal networks [36] to one in which gender equity is gradually being inserted into its social fabric, education system, and workforce [18,[37][38][39], gender is examined to assess whether it differentiates students' responses to online instruction. In this socio-cultural context, women's newly acquired rights and opportunities may make them more determined to seek academic success [40], thereby expressing no diminished enrollment and learning or even growth.…”

Is Initial Performance in a Course Informative? Machine Learning Algorithms as Aids for the Early Detection of At-Risk Students

“…Thus, how students perform in such courses can be considered key in determining their academic success, including retention and graduation [2,3]. It is an accepted fact that the timing of the identification of at-risk students is a critical aspect of the effectiveness of the implementation of remedial interventions [4][5][6]. However, educators usually have very little information about students' performance during the first half of the semester, which can make the identification of at-risk students both challenging and broadly consequential if erroneous conclusions are reached.…”