To examine the influence of different modes of distance instruction on student engagement, we drew from Self-Determination Theory (SDT) and Kahu's (2011) sociocultural model of student engagement.
Self-determination theory proposes that students are more likely to engage and learn if they feel competent to meet the challenges of their schoolwork, autonomous in that their actions stem from their own interests and values, and if they feel a sense of social belonging and relatedness in the classroom and academic community. Students must meet these needs to internalize that their academic performance stems from an internal locus of causality, take ownership of their learning, view academic behaviors as stemming from their own volition, and develop intrinsic motivation and engagement (Ryan & Deci, 2000).
Learning Environments, Belonging, and Engagement Kahu's (2011) sociocultural model of student engagement explains how learning environments can support belonging and engagement by supporting relationships between students and teachers that build affective, cognitive, and behavioral engagement. Notable indicators of engagement are feelings of interest and belonging in STEM, positive emotional states, deep cognitive engagement, and a willingness to deeply engage in course content-all of which are thought to support academic achievement, social well-being, long-term persistence and retention, and personal growth.
An essential indicator of affective engagement in this model is the need to belong. The sense of belonging to one's institution and classroom community is a key factor that motivates students to pursue and persist through undergraduate STEM programs and is associated with achievement outcomes, self-efficacy, interest, and expectancies for successparticularly for women and students of color (Anderman, 2002;Strayhorn, 2012;Goodenow, 1993;Goodenow & Grady, 1993;Gutman & Midgley, 2000;Roeser et al., 1996;Walton & Cohen, 2011).
Consistent with Kahu's (2011) model, instructors can choose online instructional methods that maximize students' sense of belonging and affective and cognitive engagement. The use of online instructional techniques that emphasize synchronous social interaction-such as live discussion, live chat, or breakout groups-could facilitate social relationships and a sense of belongingness when compared with asynchronous slide show presentations, individual work, or live presentations that limit interpersonal interactions (for a review, see Delahunty et al., 2014).
Much of the research on online teaching strategies was conducted in a time when interpersonal connections could be readily reinforced with physical infrastructure. We were interested in how engagement might be maintained and fostered by instructors in a time of forced mass transition to online learning and physical distancing. Towards this end, we conducted online focus-groups, interviews, and surveys with undergraduate students taking math-intensive STEM courses shortly after all courses transitioned online and a local lockdown was instituted during the COVID-19 pandemic. aspx?s=54-32-38-23-92-22-8F-5B-47-E1-B7-36-20-
Student surveys contained ten scales, seven of which are presented in detail in the current study. Two scales were used in our models as explanatory variables and five were included as separate response variables. Explanatory variables were student reports of online teaching methods and basic needs impacted by COVID-19 (Conway et al., 2020). Response variables were measured with scales for course belonging (Goodenow, 1993)
To answer our first research question, we tabulated and compared descriptive statistics (see Table 1). Results show that students reported that their instructors most frequently required individual work from students (Median = "80-100% of the time"), interactive live lecture (Median = "40-60% of the time"), noninteractive live lecture (Median = "40-60% of the time"), prerecorded lecture, (Median = "20-40% of the time"), breakout groups (Median = "20-40% of the time"), and least frequently engaged in synchronous discussion (Median = "0-20% of the time").
To answer our second research question, we ran three OLS regression models for each of the four response variables (belonging, emotions, cognitive engagement, and STEM interest). The first included teaching methods as explanatory variables, the second included the same predictors as well as the COVID-19 threat and impacts scale, and the third included interactions between race and teaching methods (see Table 2 for full results). For race interactions, an indicator variable was created to represent Black and Hispanic students (making up 42% of the sample, hereafter referred to as "nonwhite").
Belonging. After adjusting for COVID threat, belonging was negatively predicted by reported use of non-interactive synchronous lectures and was marginally but positively predicted by the use of breakout groups. We found similar results after including the "nonwhite" indicator and interaction terms, as well as a significant and positive interaction effect of nonwhite students who reported greater rates of interactive lectures.
Emotions. When positive emotions were the main outcome, we found that reported use of breakout groups was a positive predictor after adjusting for COVID threats and interactions. When negative emotion was the main outcome, we found that they were positively predicted by use of individual learning techniques, and marginally associated with prerecorded lecture. After including COVID threat, however, teaching methods were no longer significant predictors of negative emotions. No significant interactions with race or gender were found.
STEM Interest. The use of non-interactive lecture predicted lower reported interest in STEM for all three models. When COVID threat was included as a predictor, asynchronous individual work positively predicted STEM interest, and COVID threat negatively predicted it. No interactions with race or gender were found.
Cognitive Engagement. Cognitive engagement was positively predicted by the use of individual teaching methods for all three models. Reported use of breakout groups was significant only after adjusting for COVID threats, and there was a negative main effect of nonwhite students on cognitive engagement.
We sought to explore whether the use of different online teaching methods supported undergraduate students' psychological well-being and belongingness in their STEM courses. We found that non-interactive live lectures tended to negatively predict students' sense of belonging and interest in STEM, whereas the use of interactive live lecture and breakout groups were positive predictors of belonging, and positive emotions. That is, instructional strategies that bring students and instructors together in live interaction seems to boost affective engagement. We also found that interactive live lecture had added benefits for students of color in supporting their feelings of course belongingness, which is consistent with previous findings (see e.g., Delahunty et al., 2014).
We also found that higher reported levels of asynchronous individual work were associated with more negative emotions in some cases, and curiously, greater cognitive engagement and STEM interest overall. One explanation for this finding is that individual work may require students to exert more effort which may have been captured in the engagement measure. Generally, this finding reflects the complex nature of group vs individual work found in the existing literature showing that the effectiveness of group work is dependent on a host of contextual factors (Delahunty et al., 2014;Guerin, 2010;Micari et al., 2010). Future research might investigate why and under what conditions individual work is supportive of student engagement outcomes, and when it is not.
Our findings support the idea that students, particularly students of color, benefit from live human interaction during times of crisis. Findings from this study might be useful for college instructors and policy makers redesigning online learning environments to best support students' cognitive and emotional engagement and who aim to close the race-gap in STEM.