Abstract While data for examining learner activity are abundant, there are relatively little frameworks for principled interpretation of the behavior. Through application of Kane's argument-based 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 (n = 661) through factor analysis, item response theory, and linear regression. Results indicated that the instrument retained a three-factor 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 learners' intentions, EVC scores did not account for a significant amount of variance in learners' assessment outcomes and course behavior. Challenges remain for adjusting to learners' high expectancy and value of courses at the beginning, and for fully understanding how scores relate to learner behavior.

中文翻译：

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评估 MOOC 学习者动机的挑战：基于论证的方法的应用

摘要 虽然用于检查学习者活动的数据丰富，但对行为进行原则性解释的框架相对较少。通过应用凯恩基于论证的评估验证方法，作者进行了多项分析，将动机评估与在线行为数据相结合，以进一步验证对 MOOC 学习者的推断。EVC 动机量表在三个高级工程 MOOC 中对学习者进行管理，并从在线学习平台收集事件日志数据。通过因子分析、项目响应理论和线性回归对 EVC 项目进行了全面检查（n = 661）。结果表明，该工具保留了三因素结构以及跨年龄组和教育水平的严格结构不变性，但预期和价值项目受到显着的上限效应，这是使用这种方法评估动机的一个关键困难。作为具有学习者意图的回归模型的一部分，EVC 分数并未说明学习者评估结果和课程行为的显着差异。在开始时适应学习者对课程的高期望和价值以及充分理解分数与学习者行为的关系仍然存在挑战。