Improving MOOC quality using learning analytics and tools

Cross, Jeffrey S.; Keerativoranan, Nopphon; Carlon, May Kristine Jonson; Tan, Yong Hong; Rakhimberdina, Zarina; Mori, Hideki

doi:10.1109/lwmoocs47620.2019.8939617

Cited by 14 publications

(8 citation statements)

References 3 publications

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“…However, there are limited studies on the instructional design quality criteria of MOOCs, and several have adopted criteria based on the first principles of instruction 5 (Aloizou, Villagrá Sobrino, Martínez Monés, Asensio-Pérez & García-Sastre, 2019;Margaryan, Manuela & Littlejohn, 2015;Watson, Watson & Janakiraman, 2017). Some studies have addressed technological issues to ensure the quality of MOOCs, suggesting that natural language processing, learning analytics, and assessment tools are key factors of the effectiveness of these courses (Cross et al, 2019;Khalil, Taraghi & Ebner, 2016;Shukor & Abdullah, 2019;Yousef et al, 2014). Furthermore, attempts have been made to establish various kinds of quality rubrics or frameworks (Dyomin, Mozhaeva, Babanskaya & Zakharova, 2017;Huang, Pei & Zhu, 2017;Ma, 2018;Poce, Amenduni, Re & De Medio, 2019;Stracke, 2017;Wang, Zhao & Wan, 2017).…”

Section: Studies On the Quality Issues Of Moocsmentioning

confidence: 99%

What makes a good-quality language MOOC? An empirical study of criteria to evaluate the quality of online language courses from learners’ perspectives

Luo¹,

Ye²

2021

ReCALL

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This paper focuses on quality assurance in language massive open online courses (LMOOCs). It is a qualitative study that adopts the grounded theory method and analyses evaluative comments on the quality of LMOOCs from learners’ perspectives. With the data collected from 1,000 evaluations from English as a second language (ESL) learners on China’s biggest MOOC platform “iCourse”, this study examines what has influenced learners’ perceptions of LMOOCs and identifies the specific quality criteria of five types of them, including ESL courses for speaking, reading, writing, cultural studies, and integrated skills. The results of the study will lay a foundation for the establishment of a quality criteria framework for LMOOCs and provide insights into design principles for effective online language courses tailored to the diverse needs of a massive number of language learners.

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Section: Studies On the Quality Issues Of Moocsmentioning

confidence: 99%

What makes a good-quality language MOOC? An empirical study of criteria to evaluate the quality of online language courses from learners’ perspectives

Luo¹,

Ye²

2021

ReCALL

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“…To our best knowledge, previous studies have generally considered three quality dimensions in isolation or addressed the constructs partially in their studies (Zhou et al, 2022). Thus, the evidence-based conclusive summarization of three quality dimensions in our study will offer a comprehensive framework to the somewhat limited MOOC quality literature (Cross et al, 2019;Oh et al, 2020) to assess the course designs. Secondly, our study identifies three additional interpretable topics from the reviews: aspect-elicited consumption emotions (positive and negative) and recommendations which are extensively neglected by prior studies in the knowledge payment context (Poz on-L opez et al, 2021).…”

Section: Theoretical Implicationsmentioning

confidence: 85%

Investigating the relationships between MOOC consumers' perceived quality, emotional experiences, and intention to recommend: an NLP-based approach

Sharif

Ray

et al. 2022

OIR

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PurposeThe paper aims to explore and examine the factors that influence the post-consumption behavioral intentions of education consumers with the help of online reviews from a Massive Open Online Course (MOOC) platform in the knowledge payment context.Design/methodology/approachThe paper adopted a novel mixed-method approach based on natural language processing (NLP) techniques. Variables were identified using topic modeling drawing upon 14,585 online reviews from a global commercial MOOC platform (Udemy.com). The relationships among identified factors, such as perceived quality dimensions, consumption emotions, and intention to recommend, were then tested from a cognition-affect-behavior (CAB) perspective using partial least squares structural equation modeling (PLS-SEM).FindingsResults indicate that course content quality, instructor quality, and platform quality are strong predictors of consumers' emotions and intention to recommend. Interestingly, course content quality displays a positive effect on invoking negative emotions in the MOOC context. Additionally, positive emotions mediate the relationships between three perceived qualities and the intention to recommend.Originality/valueLimited research has been conducted regarding MOOC consumers' post-consumption intentions in the knowledge payment context. Findings of this study address the limited literature on MOOC qualities and consumer post-consumption behaviors, which contribute to a comprehensive understanding of MOOC learners' experiences at a meso-level for future paid-MOOC creators.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-09-2021-0482/

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“…Research has shown that to make the content of educational videos accessible to the widest audience possible, it is important to improve the readability of the text and captions of the videos (Cross et al, 2019). learners who are Deaf or Hard of Hearing rely on captions for access to video content.…”

Section: Caption Generation For Educational Videosmentioning

confidence: 99%

Exploring collaborative caption editing to augment video-based learning

Bhavya

Chen

Zhang

et al. 2022

Education Tech Research Dev

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Captions play a major role in making educational videos accessible to all and are known to benefit a wide range of learners. However, many educational videos either do not have captions or have inaccurate captions. Prior work has shown the benefits of using crowdsourcing to obtain accurate captions in a cost-efficient way, though there is a lack of understanding of how learners edit captions of educational videos either individually or collaboratively. In this work, we conducted a user study where 58 learners (in a course of 387 learners) participated in the editing of captions in 89 lecture videos that were generated by Automatic Speech Recognition (ASR) technologies. For each video, different learners conducted two rounds of editing. Based on editing logs, we created a taxonomy of errors in educational video captions (e.g., Discipline-Specific, General, Equations). From the interviews, we identified individual and collaborative error editing strategies. We then further demonstrated the feasibility of applying machine learning models to assist learners in editing. Our work provides practical implications for advancing video-based learning and for educational video caption editing.

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Improving MOOC quality using learning analytics and tools

Cited by 14 publications

References 3 publications

What makes a good-quality language MOOC? An empirical study of criteria to evaluate the quality of online language courses from learners’ perspectives

What makes a good-quality language MOOC? An empirical study of criteria to evaluate the quality of online language courses from learners’ perspectives

Investigating the relationships between MOOC consumers' perceived quality, emotional experiences, and intention to recommend: an NLP-based approach

Exploring collaborative caption editing to augment video-based learning

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