Negativity in Massive Online Open Courses: Impacts on Learning and Teaching and How Instructional Teams May Be Able to Address It

Comer, Denise; Baker, Ryan S.; Yuan, Wang

doi:10.46504/10201508co

Cited by 23 publications

(20 citation statements)

References 25 publications

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“…Both positive and negative emotions have been explored in the MOOC context using qualitative research approaches. Specifically, researchers have discovered altruistic and intergenerational emotions (Cheng, ) and negativity (Comer, Baker, & Wang, ) in discussion boards. However, evidence about emotional engagement beyond discussion boards is scarce, and there is no psychometric tool to quantitatively evaluate the emotional engagement of MOOC learners.…”

Section: Literature Reviewmentioning

confidence: 99%

Learner engagement in MOOCs: Scale development and validation

Deng¹,

Benckendorff²,

Gannaway³

2019

Brit J Educational Tech

145

139

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Ruiqi Deng is a PhD candidate at the University of Queensland. His research interests are MOOCs, online learning environment design and technology-enhanced learning. He received a bachelor's degree from the University of Queensland and a master's degree from Department of Education, University of Oxford. Pierre Benckendorff is an associate professor at the University of Queensland. His research interests are tourism education and scholarship, tourism information technology, and visitor behaviour. He has authored over 90 publications in these areas in leading international journals and is a regular speaker at tourism and higher education research conferences. Deanne Gannaway is a senior lecturer at the University of Queensland. Her research interests are curriculum design, educational evaluation and teaching innovation. She is the program coordinator for professional learning in teaching and is regularly invited to facilitate workshops and seminars across Australasia and beyond. AbstractThe purpose of this study is to contribute to a better understanding of the complexity of conceptualising and measuring learner engagement in Massive Open Online Courses (MOOCs). The paper develops and validates a MOOC engagement scale (MES) to measure learner engagement. The initial questionnaire items of the scale were developed by reviewing existing related literature and validated student engagement constructs. This was followed by a modified scale development process, including two focus group interviews (n = 10), an exploratory survey (n = 12), an expert review (n = 10), a pilot survey (n = 15), an item purification study (n = 590) and a construct validation study (n = 303). The final version of the scale is made up of four dimensions: behavioural engagement, cognitive engagement, emotional engagement and social engagement. The paper concludes with recommendations for how the MES can be used to assess the pattern of engagement in MOOCs and to investigate the relationship between learner engagement and other important MOOC teaching and learning factors. InstructionThink about a MOOC you enrolled in and keep this MOOC in mind when answering all the questions. Indicate to what extent you agree with the statements on a 6-point Likert scale:

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Section: Literature Reviewmentioning

confidence: 99%

Learner engagement in MOOCs: Scale development and validation

Deng¹,

Benckendorff²,

Gannaway³

2019

Brit J Educational Tech

145

139

View full text Add to dashboard Cite

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“…This dimension of engagement has been called “emotional engagement” in the broader education literature and represents students' positive and negative reactions to teachers, classmates, classroom activities and schools (Fredricks et al, 2004). MOOC studies centred on emotional engagement have uncovered both positive emotions, such as altruism (Cheng, 2014) and negative emotions, such as negativity (Comer, Baker, & Wang, 2015), by analysing the interaction transcripts collected from the discussion boards. Finally, a few researchers have investigated the self‐regulated learning behaviours undertaken by MOOC participants (Littlejohn et al, 2016; Milligan & Littlejohn, 2016).…”

Section: Literature Reviewmentioning

confidence: 99%

Linking learner factors, teaching context, and engagement patterns withMOOClearning outcomes

Deng

Benckendorff

Gannaway

2020

Computer Assisted Learning

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Prior studies of MOOC learners have focused almost exclusively on behavioural and social aspects of engagement. This paper extends the scope of previous studies by adopting a multi‐dimensional, person‐centred approach to investigate learner engagement in MOOCs. An analysis of 1,452 self‐administered survey responses uncovered three prototypical categories of MOOC learners based on patterns of behavioural, cognitive, emotional and social engagement: (a) “Individually Engaged” learners, (b) “Least Engaged” learners and (c) “Wholly Engaged” learners. The study revealed significant differences among the three cohorts of MOOC participants with respect to learner factors (gender, origin, motivation), teaching context (course level, course duration, form of the assessment) and learning outcomes (course completion, perceived quality of instruction). The results of this study suggest that adopting a multi‐dimensional, person‐centred approach can be useful for researchers and practitioners to classify MOOC learners into subpopulations, design effective educational interventions that best engage different types of learners, and provide support and scaffolding to individuals with idiosyncratic or problematic engagement patterns.

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“…There are two challenges associated with the dataset that is inherent to the nature of the problem under study. Although the expression of neg-ative or deactivating emotions is common within learning (Pekrun et al, 2002), verbally abusive behaviors are less common, although still detrimental (Mak et al, 2010;Comer et al, 2015). As indicated in our dataset, a small percentage of messages (3.02%) coded as "negative", resulted in a highly imbalanced dataset, which could have negative effects on the classification results.…”

Section: Model Implementationmentioning

confidence: 73%

“…To understand the nature of negativity in MOOCs, we draw on the work by Comer and her colleagues (2015) who discuss three types of negativity in MOOCs: negativity towards i) the course, ii) instructor, and iii) course platform. This multifaceted perspective demonstrates that the main sources of negativity are associated with pedagogy or course design decisions and cannot be easily addressed during course facilitation (Comer et al, 2015). Despite the relatively low proportion of abusive behaviors in MOOCs, Comer and colleagues illustrate the negative impacts they have on instructor presence and the broader levels of participation in discussion forums.…”

Section: Roots Of Negativity In Moocsmentioning

confidence: 99%

Automated Identification of Verbally Abusive Behaviors in Online Discussions

Joksimovíc¹,

Baker²,

Ocumpaugh³

et al. 2019

Proceedings of the Third Workshop on Abusive Language Online

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Discussion forum participation represents a crucial support for learning and often the only way of supporting social interactions in online settings. However, learner behavior varies considerably in these forums, including positive behaviors such as sharing new ideas or asking thoughtful questions, but also verbally abusive behaviors, which could have disproportionate detrimental effects. To provide means for mitigating potential negative effects on course participation and learning, we developed an automated classifier for identifying communication that show linguistic patterns associated with hostility in online forums. In so doing, we employ several wellestablished automated text analysis tools and build on common practices for handling highly imbalanced datasets and reducing sensitivity to overfitting. Although still in its infancy, our approach shows promising results (AUC ROC=0.74) towards establishing a robust detector of abusive behaviors. We provide an overview of the classification (linguistic and contextual) features most indicative of online aggression.

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Negativity in Massive Online Open Courses: Impacts on Learning and Teaching and How Instructional Teams May Be Able to Address It

Cited by 23 publications

References 25 publications

Learner engagement in MOOCs: Scale development and validation

Learner engagement in MOOCs: Scale development and validation

Linking learner factors, teaching context, and engagement patterns withMOOClearning outcomes

Automated Identification of Verbally Abusive Behaviors in Online Discussions

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