Instructional design, facilitation, and perceived learning outcomes: an exploratory case study of a human trafficking MOOC for attitudinal change

Watson, Sunnie Lee; Loizzo, Jamie; Watson, William R.; Mueller, Chad; Lim, Jieun; Ertmer, Peggy A.

doi:10.1007/s11423-016-9457-2

Cited by 81 publications

(67 citation statements)

References 33 publications

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“…In contrast, Watson et al . () argued that the MOOC instructor's role as facilitator is critical in xMOOCs.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

The effects of openness, altruism and instructional self‐efficacy on work engagement of MOOC instructors

Doo¹,

Zhu²,

Bonk³

et al. 2019

Brit J Educational Tech

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Many of the characteristics and nature of teaching massive open online courses (MOOCs) are different from face‐to‐face teaching, which can directly affect instructors’ work engagement and ultimately the success of MOOCs. As such, this study investigated the effects of openness, altruism and instructional self‐efficacy on MOOC instructors’ work engagement. A total of 209 MOOC instructors participated in an online survey, and their responses were analyzed. The research findings indicated that openness to experience influenced MOOC instructors’ instructional self‐efficacy and work engagement. Altruism did not directly influence work engagement of MOOC instructors; however, it indirectly affected work engagement through instructional self‐efficacy. Instructional self‐efficacy also affected MOOC instructors’ work engagement and fully mediated the relationship between altruism and work engagement. The variables examined in this study, openness, altruism and instructional self‐efficacy, were found to significantly influence work engagement of MOOC instructors. As the number of MOOCs and MOOC students increase, the importance of MOOC instructors and their work engagement will be crucial for the success of these courses. Thus, additional research is needed on the ways to enhance MOOC instructors’ work engagement. What is already known about this topic The number of MOOCs and institutions offering MOOCs has grown rapidly since 2008 to more than 11 400 MOOCs at over 900 universities in 2018. MOOC instructors play a major role in the success of MOOCs. Work engagement has a significant impact on performance and attitudes, including job satisfaction, organizational commitment and turnover intention. What this paper adds Openness to experience, altruism and instructional self‐efficacy significantly influenced work engagement of MOOC instructors. Openness to experience influenced MOOC instructors’ instructional self‐efficacy and work engagement. Altruism did not directly influence work engagement of MOOC instructors; however, it indirectly affected work engagement through instructional self‐efficacy. Instructional self‐efficacy also affected MOOC instructors’ work engagement and fully mediated the relationship between altruism and work engagement. Implications for practice and/or policy It is important to arouse MOOC instructors’ intellectual curiosity and creativity. To enhance MOOC instructors’ work engagement, it is necessary to enhance their instructional self‐efficacy as a prerequisite for successful MOOC teaching. There is a need to provide MOOC instructors with the necessary information and opportunities to sharpen the skills required for teaching MOOCs effectively through MOOC instructor seminars or workshops. Providing opportunities to teach a small online class first can improve MOOC instructors’ instructional self‐efficacy for MOOC teaching.

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“…In contrast, Watson et al . () argued that the MOOC instructor's role as facilitator is critical in xMOOCs.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

The effects of openness, altruism and instructional self‐efficacy on work engagement of MOOC instructors

Doo¹,

Zhu²,

Bonk³

et al. 2019

Brit J Educational Tech

View full text Add to dashboard Cite

show abstract

“…The fast-developing context of research on LA and MOOCs shows a variety of emerging themes (e.g., Peña-Ayala, 2018;Veletsianos & Shepherdson, 2016) such as MOOC design (Watson et al, 2016), student subpopulations (Kizilcec, Piech, & Schneider, 2013), or student motivation (Koller, Ng, Chuong, & Zhenghao, 2013). However, despite existing research on MOOC design (Watson et al, 2016), it appears that many MOOCs are developed without applying basic instructional design principles (Margaryan, Bianco, & Littlejohn, 2015) as formulated by Reigeluth (2016), for instance. Furthermore, the "funnel of participation" described as going from awareness to registration, activity, progress, and for some learners, even completion (Clow, 2013), leads to attention toward student dropout (e.g., Kahan, Soffer, & Nachmias, 2017).…”

Section: Background and Related Workmentioning

confidence: 99%

Analysing Structured Learning Behaviour in Massive Open Online Courses (MOOCs): An Approach Based on Process Mining and Clustering

Beemt

Buijs

Aalst

2018

IRRODL

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The increasing use of digital systems to support learning leads to a growth in data regarding both learning processes and related contexts. Learning Analytics offers critical insights from these data, through an innovative combination of tools and techniques. In this paper, we explore students’ activities in a MOOC from the perspective of personal constructivism, which we operationalized as a combination of learning behaviour and learning progress. This study considers students’ data analyzed as per the MOOC Process Mining: Data Science in Action. We explore the relation between learning behaviour and learning progress in MOOCs, with the purpose to gain insight into how passing and failing students distribute their activities differently along the course weeks, rather than predict students' grades from their activities. Commonly-studied aggregated counts of activities, specific course item counts, and order of activities were examined with cluster analyses, means analyses, and process mining techniques. We found four meaningful clusters of students, each representing specific behaviour ranging from only starting to fully completing the course. Process mining techniques show that successful students exhibit a more steady learning behaviour. However, this behaviour is much more related to actually watching videos than to the timing of activities. The results offer guidance for teachers.

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“…Previous studies argued that self-monitoring can improve students' performance (Chang, 2007;Coleman & Webber, 2002). In addition, some studies have indicated that teaching self-monitoring skills can benefit learners (e.g., Delclos & Harrington, 1991;Maag et al, 1992;Malone & Mastropieri, 1992;Schunk, 1982) However, as several researchers have pointed out, the instructional design and actual delivery of MOOCs from the perspective of MOOC instructors are significantly underexamined (Margaryan, Bianco, & Littlejohn, 2015;Ross, Sinclair, Knox, Bayne, & Macleod, 2014;Watson et al, 2016;Zhu, Sari, & Lee, 2018); especially lacking is research on instructor perceptions in terms of facilitating student self-monitoring for SDL.…”

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confidence: 99%

Designing MOOCs to Facilitate Participant Self-monitoring for Self-directed Learning

Zhu

Bonk

2019

OLJ

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This study investigated the design and delivery of MOOCs to facilitate student self-monitoring for self-directed learning (SDL) using mixed methods. The data sources of this study included an online survey with 198 complete respondents, semistructured interviews with 22 MOOC instructors, and document analysis of 22 MOOCs. Study results indicated that MOOC instructors considered self-monitoring skills critical for SDL. To foster students' self-monitoring, MOOC instructors reported that they facilitated students' self-monitoring by helping students with internal feedback and providing external feedback. Students' internal feedback included cognitive and metacognitive processes. To facilitate cognitive processes, MOOC instructors provided quizzes, tutorials, learning strategies, learning aids, and progress bars. For metacognition, these instructors provided reflection questions and attempted to create learning communities. In addition, MOOC instructors, teaching assistants, and peers provided external feedback for students' self-monitoring. Across these findings, technology played a central role in supporting students' self-monitoring.

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Instructional design, facilitation, and perceived learning outcomes: an exploratory case study of a human trafficking MOOC for attitudinal change

Cited by 81 publications

References 33 publications

The effects of openness, altruism and instructional self‐efficacy on work engagement of MOOC instructors

The effects of openness, altruism and instructional self‐efficacy on work engagement of MOOC instructors

Analysing Structured Learning Behaviour in Massive Open Online Courses (MOOCs): An Approach Based on Process Mining and Clustering

Designing MOOCs to Facilitate Participant Self-monitoring for Self-directed Learning

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