Online education continues to become an increasingly prominent part of higher education, but many students struggle in distance courses. For this reasonFor this reason, there has been considerable interest in predicting which students will succeed in online courses , achieving poor grades or dropping out prior to course completionn). Effective intervention depends on understanding which students are at-risk in terms of actionable factors, and behavior within an online course is one key potential factor for intervention. In recent years, many have suggested that Massive Online Open Courses (MOOCs) are a particularly useful place to conduct research into behavior and interventions, given both their size and the relatively low consequences/costs of experimentation. However, it is not yet clear whether the same factors are associated with student success in open courses such as MOOCs as in for-credit course -- an important consideration before transferring research results between these two contexts. While there has been considerable research in each context, differences between course design and population limit our ability to know how broadly findings generalize; differences between studies may have nothing to do with whether students are taking a course for-credit or as a MOOC. Do , this body of literature has been split into two-subcategories: research on success in MOOCs and research on success in For-credit courses. Few studies Few studies have attempted tohave attempted to understand how students and their learning experiences differ between these contexts, bypassing an opportunity to synthesize findings across different student populations who engage in online education. Do bypassing an opportunity to synthesize findings across different populations who engage in online education. To address this issue, we Do learners behave the same way in MOOCs and for-credit courses? AAre the implications for learning different, even for the exact same behaviors? In this paper, we study these issues through developing models that predict student course success from online interactions, in an online learning platform that caters to both distinct student groups (i.e., students who enroll on a for-credit or a non-credit basis). Our findings indicate that our models perform well enough to predict students’ course grades for new students across both of our populations. Furthermore, models trained on one of the two populations were able to generalize to new students in the other student population. We find that features related to comments were good predictors of student grade for both groups. Models generated from this research can now be used by instructors and course designers to identify at-risk students both for-credit and MOOC learners, towards providing both groups with better support.
Application of Multimedia Network Technology in Chinese EFL Learners' Autonomous Learning of English Phonetics
