Explaining Chinese university studentsxe2x80x99 continuance learning intention in the MOOC setting: A modified expectation confirmation model perspective Abstract   To gain more insight into the issue of high dropout rate in MOOC learning, this study aims at exploring the factors underlying the continuance intention to learn in the Massive Open Online Course (MOOC) setting. By modifying and extending the Expectation Confirmation Model (ECM), the authors propose a research model that includes cognitive and affective variables, captures reflections of the past and expectations for the future and takes into account both intrinsic and extrinsic motives in the model construction to explain learnersu0027 intention to persist in learning a MOOC. The proposed model was tested with data from Chinese university students. The results show that the proposed model can explain 48% of continuance intention. The new variables (attitude and curiosity) added to the ECM were all found to be significant in explaining continuance intention. This study deepens our understanding of the development of learnersu0027 continuance intention in the MOOC setting in the following aspects: (a) although the personal trait, curiosity, was found to predict subsequent continuance intention, attitude played a considerably dominant role. In addition to respecting individual differences, practitioners can devise appropriate interventions to change attitudes and influence learnersu0027 retention in MOOCs; (b) the strong link between confirmation and both satisfaction and attitude suggests that MOOC instructors or designers must be prudent in advertising the courses to avoid exaggerating their benefits and the systemu0027s affordances.

Challenges to assessing motivation in MOOC learners: An application of an argumentbased approach Abstract   While data for examining learner activity are abundant, there are relatively little frameworks for principled interpretation of the behavior. Through application of Kaneu0027s argumentbased approach to assessment validation, the authors conducted several analyses to combine motivation assessment with online behavioral data for further validating inferences made about MOOC learners. The EVC motivation scale was administered to learners in three advanced engineering MOOCs, and event log data was collected from the online learning platform. The EVC items were comprehensively examined (nxe2x80xafxe2x80xaf661) through factor analysis, item response theory, and linear regression. Results indicated that the instrument retained a threefactor structure as well as strict and structural invariance across age groups and education levels, but the Expectancy and Value items suffered from significant ceiling effects, a key difficulty in assessing motivation using this approach. As part of a regression model with learnersu0027 intentions, EVC scores did not account for a significant amount of variance in learnersu0027 assessment outcomes and course behavior. Challenges remain for adjusting to learnersu0027 high expectancy and value of courses at the beginning, and for fully understanding how scores relate to learner behavior.