Examining the Relations among Student Motivation, Engagement, and Retention in a MOOC: A Structural Equation Modeling Approach

Students who are enrolled in MOOCs tend to have different motivational patterns than fee-paying college students. A majority of MOOC students demonstrate characteristics akin more to "tourists" than formal learners. As a consequence, MOOC studentsΓÇÖ completion rate is usually very low. The current study examines the relations among student motivation, engagement, and retention using structural equation modeling and data from a Penn State University MOOC. Three distinct types of motivation are examined: intrinsic motivation, extrinsic motivation, and social motivation. Two main hypotheses are tested: (a) motivation predicts student course engagement; and (b) student engagement predicts their retention in the course. The results show that motivation is significantly predictive of student course engagement. Furthermore, engagement is a strong predictor of retention. The findings suggest that promoting student motivation and monitoring individual studentsΓÇÖ online activities might improve course retention

Peer Assessment for Massive Open Online Courses (MOOCs)

The teach-learn-assess cycle in education is broken in a typical massive open online course (MOOC). Without formative assessment and feedback, MOOCs amount to information dump or broadcasting shows, not educational experiences. A number of remedies have been attempted to bring formative assessment back into MOOCs, each with its own limits and problems. The most widely applicable approach for all MOOCs to date is to use peer assessment to provide the necessary feedback. However, unmoderated peer assessment results suffer from a lack of credibility. Several methods are available today to improve on the accuracy of peer assessment results. Some combination of these methods may be necessary to make peer assessment results sufficiently accurate to be useful for formative assessment. Such results can also help to facilitate peer learning, online discussion forums, and may possibly augment summative evaluation for credentialing

Assessing the impact of the local environment on birth outcomes: A case for HLM

Hierarchical linear Models (HLM) is a useful way to analyze the relationships between community level environmental data, individual risk factors, and birth outcomes. With HLM we can determine the effects of potentially remediable environmental conditions (e.g., air pollution) after controlling for individual characteristics such as health factors and socioeconomic factors. Methodological limitations of ecological studies of birth outcomes and a detailed analysis of the varying models that predict birth weight will be discussed. Ambient concentrations of criterion air pollutants (e.g., lead and sulfur dioxide) demonstrated a sizeable negative effect on birth weight; while the economic characteristics of the mother\u27s residential census tract (ex. poverty level) also negatively influenced birth weight. © 2007 Nature Publishing Group All rights reserved

Examining the Relations among Student Motivation, Engagement, and Retention in a MOOC: A Structural Equation Modeling Approach

Students who are enrolled in MOOCs tend to have different motivational patterns than fee-paying college students. A majority of MOOC students demonstrate characteristics akin more to "tourists" than formal learners. As a consequence, MOOC students’ completion rate is usually very low. The current study examines the relations among student motivation, engagement, and retention using structural equation modeling and data from a Penn State University MOOC. Three distinct types of motivation are examined: intrinsic motivation, extrinsic motivation, and social motivation. Two main hypotheses are tested: (a) motivation predicts student course engagement; and (b) student engagement predicts their retention in the course. The results show that motivation is significantly predictive of student course engagement. Furthermore, engagement is a strong predictor of retention. The findings suggest that promoting student motivation and monitoring individual students’ online activities might improve course retentio