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In Massive Open Online Courses (MOOCs), learners can post both text comments and overall ratings regarding the courses. There is growing interest in assessing the consistency of online reviews and the determinants of learner satisfaction. This study analyses the disconfirmation effect between textual review topics and the determinants of learner satisfaction in MOOCs. The MOOCs are categorised under three disciplines - Social Science, Technical Science, and Humanities & Natural Science. A crawler was employed to collect the corpus, extracting 93,679 reviews of 5,214 online courses from a Chinese university MOOC platform (icourse163.org). Textual analytics was used in the topic extraction. The empirical results suggest a strong disconfirmation effect between textual reviews and the determinants of learner satisfaction, i.e., not all textual review topics affect the overall learner satisfaction. Compared with positive reviews, negative (and neutral) reviews have a stronger disconfirmation effect. Further, the antecedents of learner attention are course-discipline specific. The disconfirmation effect is course-discipline dependent, with the most prominent for Technical Science courses, and the least for Humanities & Natural Science courses. This study provides a framework to guide platform managers and course instructors in better course delivery and enhancing overall learner satisfaction.

Learners engaged in large-scale online learning often pose questions in which their peers or instructors can answer using various means of textual interaction topics. This paper assesses the effects of the text interaction strategies in online learning through the lens of the language expectancy theory at three levels: whether to respond to the questions, the identity of the respondents, and the textual interaction topics. Using 112,680 learning records of 610 courses from 71,948 learners crawled from the online learning programming platform iMOOC as the corpus, text mining is used to identify the interaction strategies. Using grounded theory, the textual interaction topics are divided into 2 groups (providing solutions, and encouragement & evaluation for the learners), and sub-divided into 6 topic clusters ("code writing," "operation guidance," "providing references," "encouragement," "normative interpretation," and "opinion exchange"). The responses are classified by text mining. The results of the econometric model suggest that responding to the questions online fosters learning and reduces the dropout rate. The online learner benefits more from peer learning than from the instructors. On the text interaction topics, the topic "providing solutions" is more effective in reducing the learner's dropout rate than the topic "encouragement & evaluation". Further, "code writing" is more effective over "providing references," "encouragement," and "normative interpretation." This study enriches our understanding of the interaction strategies between learners and instructors in iMOOC, and provides a reference for improving the online learning journey and retain learners.

This study analyzed the influence of rhetoric in the endorsement text on the willingness of the crowd to participate in citizen science projects. Four categories of endorsers were studied: professors, students, industrial researchers, and amateur researchers. Using 1243 endorsement texts from 543 citizen science projects as the corpus, the effects of the standalone techniques (ethos, pathos, and logos) and mixed-rhetoric techniques of persuasion were examined empirically. The results informed that pathos and logos had significant advantages over ethos. Taking a mixed-rhetoric approach to the endorsement text with a mix of pathos (55%) and logos (45%) maximized the appeal of citizen science projects, and the influence of this approach had an inverted U-shaped effect. On the identity of the endorsers, the professors, students, and amateur researchers shared a similar rhetorical approach, while the industrial researchers shared another. However, from the influence of endorsement rhetorical strategies, the impact of amateur researchers was consistent with that of the industrial researchers (both pathos and logos were positive); and professors (only logos was positive) and students (only pathos was positive) reveal some differences. Finally, endorsement rhetoric strategies exerted a greater influence on the humanities and social science projects than on the natural science projects.

This study investigated 150 university lecturers from the perspective of their emotional labour, focusing on the effect of a playful climate on lecturers' innovation intention and job engagement. A structural equation model analysis determined the following: is positively related to lecturers' strategies of deep acting and expression of natural emotions, but its effect on surface action is not significant; deep acting partially mediates between university playfulness climate and innovation intention, and the expression of natural emotions partially mediates between university playfulness climate and job engagement. This study expands our knowledge regarding the effect of emotional labour on lecturers' individual positive performance and enriches our understanding of the effect of playfulness climate on teaching activities.