What Lies beneath Sustainable Education? Predicting and Tackling Gender Differences in STEM Academic Success

Gender impact on STEM online learning- a correlational study of gender, personality traits and learning styles in relation to different online teaching modalities

STEM (science, technology, engineering and mathematics) education benefits both individuals and society. It supports individuals by increasing their critical-thinking skills, encouraging creativity, as well as providing a basis for new inventions. The underrepresentation of women in STEM is a complex issue with various causes and different approaches of addressing it, where most likely gender differences are caused by desires and choice rather than abilities and performance. This paper explores differences in online and traditional STEM learning based on gender. It examines in detail recently identified patterns of women's success, their access to STEM online courses, and their overall course experience during such courses. We analyzed results from a case study in which students were enrolled for one semester in two STEM online courses and completed questionnaires about their character traits and learning styles and how they relate to academic performance. The objective of our research is to analyze academic success during traditional classes and online classes, with focus on gender and identify how character traits and learning styles correlate with gender in online classes. The main outcome of our research is that female students, which study in the field of STEM in particular computer science, are trustworthy and autonomous students who can outperform their male counterparts during traditional courses, where during online courses male students still exceed slightly female students. The trait of Consciousness is a success predictor regardless of gender and learning environment, while the trait of Neuroticism has negative impact the traditional learning environment, Extraversion shows negative impact in online learning. Learning styles show gender differences, where female students prefer the style of read/write while male students favor kinesthetic.

Adaptive Individual Differences in Math Courses

A higher education that can be defined as sustainable ensures the acquisition of competencies that are necessary to address the current and future needs of the society in which it exists. Because math competencies are an essential component of college students’ academic and professional success, poor performance outcomes are particularly problematic in the context of an education that aims to be sustainable. This research sought to identify dispositions that are predictive of math performance in the post-pandemic world to develop an early detection system for at-risk students of an understudied population (college students of Middle Eastern descent from Saudi Arabia). It specifically targeted female and male students in STEM or non-STEM majors who were enrolled in a math course of the general education curriculum. During the second semester of a return to entirely face-to-face instruction, their self-efficacy, math learning anxiety, math evaluation anxiety, and preference for morning or evening study activities were surveyed. In the post-pandemic world of this understudied population, the math performance of STEM male and female students was hurt by concerns about learning math. The math performance of non-STEM male students benefited from self-efficacy, whereas that of non-STEM female students was unaffected by any of the dispositions surveyed in the present investigation. These findings suggest that individual difference measures can inform early interventions intended to address performance deficiencies in selected groups of students with the overreaching goal of ensuring a sustainable education for all.

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

The extent to which grades in the first few weeks of a course can predict overall performance can be quite valuable in identifying at-risk students, informing interventions for such students, and offering valuable feedback to educators on the impact of instruction on learning. Yet, research on the validity of such predictions that are made by machine learning algorithms is scarce at best. The present research examined two interrelated questions: To what extent can educators rely on early performance to predict students’ poor course grades at the end of the semester? Are predictions sensitive to the mode of instruction adopted (online versus face-to-face) and the course taught by the educator? In our research, we selected a sample of courses that were representative of the general education curriculum to ensure the inclusion of students from a variety of academic majors. The grades on the first test and assignment (early formative assessment measures) were used to identify students whose course performance at the end of the semester would be considered poor. Overall, the predictive validity of the early assessment measures was found to be meager, particularly so for online courses. However, exceptions were uncovered, each reflecting a particular combination of instructional mode and course. These findings suggest that changes to some of the currently used formative assessment measures are warranted to enhance their sensitivity to course demands and thus their usefulness to both students and instructors as feedback tools. The feasibility of a grade prediction application in general education courses, which critically depends on the accuracy of such tools, is discussed, including the challenges and potential benefits.

Predicting Math Performance of Middle Eastern Students: The Role of Dispositions

The present research examines the contribution of individual differences in chronotype and self-efficacy to the math performance of male and female students in STEM and no-STEM majors. Questionnaires assessing the selected individual differences were distributed to students of Middle Eastern descent enrolled in math courses of the general education curriculum. Summative assessment indices were used to measure performance comprehensively across the entire semester (course grades) and as a one-time occurrence (final test grades). The contribution of morningness and self-efficacy to both course and test performance of STEM students was sensitive to the interaction of gender and major. Instead, neither factor contributed to no-STEM students’ course and test performance. These findings were used to plan improvements in the instruction and advising of students in STEM majors, thereby complying with a key tenet of action research.

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

A sustainable education amid a disruptive event (e.g., a pandemic) requires the objective assessment of learning before and during the event and, if necessary, evidence-driven solutions in response to deficiencies. The present action research study illustrates an evidence-based response of educators to the widespread concerns that learning in college students, accustomed to face-to-face courses, might have been damaged during the pandemic by the switch to the online mode. It focuses on general education (GE) courses as they usually enroll students at the beginning of their journey in higher education, and thus, a population that is likely to be particularly sensitive to unforeseen changes. Pass/fail grades in courses taught face-to-face and online synchronously by the same instructors were examined. It was hypothesized that if the switch from face-to-face to online instruction changed the students’ approaches to learning, course performance would differ between the instructional modes. Differences in female and male students’ adaptation responses were expected to be reflected in their course performance. The study found that female students performed better online than face-to-face in Arabic Culture, Natural Science, Math, and Wellness courses. Male students also performed better online in Math and Natural Science courses, whereas they exhibited better performance face-to-face in Arabic Culture, Wellness, and Professional Competency courses. It was concluded that basic indices of uneven performance can guide further analyses into the sources of female and male students’ approaches to instructional modes.

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

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.