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This analysis uses data from our recent Remote Learning Survey to identify the best predictor of students’ remote learning experience and perception (henceforth RLEP). Over 4,500 NU students (74%) participated in the survey. We used Exploratory Factor Analysis to construct an indicator of RLEP based on 12 key survey items. We then classified each student as having a positive or negative RLEP. Finally, we used data mining algorithms to determine which single factor (among 21 candidates) best predicts RLEP.
Motivation (i.e., the extent to which students experienced/perceived it as a challenge) emerged as the single best predictor of RLEP. This factor alone predicted whether the student had a positive or negative EPRL with a 72% accuracy. Motivation had been identified as a major challenge by 52% of the students (with 31% identifying it as a minor challenge and 17% as not being a challenge). A large proportion of undergraduate students, more particularly, had identified it as a major challenge (Figure 1).
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| As Figure 2 suggests, the probability of having a positive RLEP was substantially low for students who had identified motivation as a major challenge: only 33% of these students had a positive RLEP. Undergraduate students accounted for two-thirds of this group and were, consequently, a lot less likely to have a positive RLEP compared to other students. In fact, only 42% of undergraduate students (vs. 74% graduate and 82% CPS students) had a positive RLEP. |
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| In sum, motivation is crucial for a positive RLEP. The more students struggle with motivation, the less likely they are to have a positive RLEP. Interventions seeking to foster student adaptation to remote learning need to also target student motivation, mindset, and attitude towards remote learning. An important question is: what interventions/practices are most likely to lead to increased motivation for remote learning? |
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The IR-MFF is published every last Friday of the month (August through November and January through April). Depending on circumstances, it can be published before or after the last Friday of the month. Analyses are limited to 300 words or less (narrative text) and one or two graphs/tables. If you need more detailed information (methodological or otherwise), please contact Institutional Research and Analytics at ir@nu.edu.kz.
