Modelling MOOC learners' social behaviours

Sunar, Ayşe Saliha; Abbasi, Rabeeh Ayaz; Davis, Hugh C.; White, Su; Aljohani, Naif Radi

doi:10.1016/j.chb.2018.12.013

Cited by 31 publications

(20 citation statements)

References 10 publications

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“…According to Jovanovic et al [56], a learning strategy includes any thoughts, behaviors, beliefs, or emotions that facilitate the acquisition, understanding, or later transfer of new knowledge and skills that have to be mined/detected using appropriate analytical methods and techniques. Essentially, e-learning analytics leverages digital trace data from online systems by which feedback is provided when a lecture video is watched, a quiz is completed, or a task is posted on the forum discussion board [57].…”

Section: Discussionmentioning

confidence: 99%

Could MOOC-Takers’ Behavior Discuss the Meaning of Success-Dropout Rate? Players, Auditors, and Spectators in a Geographical Analysis Course about Natural Risks

et al. 2020

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Research interest in massive online and open courses (MOOCs) is rapidly growing, questioning who enrolls, why and how to conceive engagement, and success rates. This study is focused on MOOC-takers behavior obtained from a seven-week MOOC experience on natural risks. Data scraping principles have been used to collect data. Demographics, success-dropout rates, engagement periods, achievement and scoring, and behavior were analyzed through descriptive statistics, non-parametric correlation analysis, and statistical hypothesis testing. The results show that students who start earlier and those who finish earlier the course obtain better grades in some of the modules (motivation and background on natural risks could be the explanation). However, for ‘last moment students’, speed in passing the modules is either related to greater motivation, although in this case it is not related to better grades. Furthermore, students who complete tasks during the weekend take less time to complete the modules and obtain a better grade. In addition, a learning strategy is promoted by reconsidering who is learning: players (those who complete the course and earning a certificate), auditors (those who have completed a thematic unit or the whole module, earning partial knowledge), and spectators (those enrolled until the end of the course, who intend earning experience in e-learning).

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Section: Discussionmentioning

confidence: 99%

Could MOOC-Takers’ Behavior Discuss the Meaning of Success-Dropout Rate? Players, Auditors, and Spectators in a Geographical Analysis Course about Natural Risks

et al. 2020

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“…Such online operational behaviors, once properly interpreted, can reflect the students' online learning behaviors. Online learning behaviors are mostly calculated on the basis of frequency and time (Shang et al, 2020), including the total frequency of which a course was accessed (Asarta & Schmidt, 2013), the total frequency and total duration of which a resource was accessed (Morris et al, 2005), the total frequency or duration of which a teaching video was accessed (Lin et al, 2017;Lust et al, 2013;Ziebarth & Hoppe, 2014), the time of which a test was taken (Li & Baker, 2018), and the total number of posts created in online discussion (Sunar et al, 2018). After researchers collect these online learning behaviors, data cleaning must be performed to prevent bias caused by abnormal values (Agrawal et al, 2014), and the adequacy of the behavioral data collection must be verified (Dumais et al, 2014).…”

Section: Online Learning Behaviorsmentioning

confidence: 99%

Effects of the COVID-19 pandemic on the online learning behaviors of university students in Taiwan

2021

Educ Inf Technol

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Prior to the coronavirus disease 2019 (COVID-19) pandemic, due to the rarity of pandemics in recent centuries, suitable conditions did not exist in educational institutions for the implementation of asynchronous distance teaching. No empirical studies have been conducted on whether the considerable environmental changes caused by COVID-19 have affected students’ online learning behaviors. Therefore, this study collected information on students’ online learning behaviors during the COVID-19 pandemic and other periods to examine whether pandemic-caused environmental changes affected students’ online learning behaviors. This study focuses on the 60-day transmission after the beginning of the second semester of the 2019 academic year. The data source was from a comparative assessment between the pandemic group (331 students) and the control group (101 students). The Spearman Rank Correlation Test and the Wilcoxon signed-rank test were used as our statistical methods. This paper presents preliminary results on how COVID-19 has affected students’ online learning behaviors and proposes asynchronous online learning as a method for maintaining university students’ learning during the COVID-19 pandemic.

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“…Given that self-disclosure is context-specific [14], would disclosing a personal name in a learning context imply something other than the expectation of social networking since the number of in-depth peer interactions in MOOCs is found to be quite low [20]?.…”

Section: Literature Review a Social Psychological Theories Of Self-disclosurementioning

confidence: 99%

Disclosing Personal Names in Screen Names Predicts Better Final Achievement Levels in Massive Open Online Courses

et al. 2021

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The anonymity of the Internet used to be considered as an encouraging factor that helped learners engage in online learning. However, academic studies on anonymity have found that its effect on learning is context-dependent or mixed. In this research, we focused on massive open online course (MOOC) learners' preference for personal name disclosure in their screen names as a predictor of their final achievement levels (FALs) at the end of a course including 2606 active learners. We conducted two studies, one to examine the associations between these two variables and one to demonstrate how such associations can be utilized in MOOC FAL prediction. We found that MOOC learners who included personal names in their MOOC screen names significantly outperformed other learners in their FALs (p < 0.001). We also found that screen name preference improved FAL prediction accuracy utilizing natural language processing and proper machine learning technologies. The error rate was reduced to 4.03% by a random forest algorithm with an appropriate feature combination: the personal name disclosure indicator (PNDI), quiz scores, number of replies, and exam scores. The results are potentially useful for the development of an early intervention to provide different types of help to students who prefer to disclose personal names and those who do not. The practical effects of these interventions will be examined in the future. In addition, whether the course difficulty level or course type affects the associations between personal name disclosure and FAL will also be examined.INDEX TERMS Final achievement level, machine learning, personal name disclosure, prediction, screen name.

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Modelling MOOC learners' social behaviours

Cited by 31 publications

References 10 publications

Could MOOC-Takers’ Behavior Discuss the Meaning of Success-Dropout Rate? Players, Auditors, and Spectators in a Geographical Analysis Course about Natural Risks

Could MOOC-Takers’ Behavior Discuss the Meaning of Success-Dropout Rate? Players, Auditors, and Spectators in a Geographical Analysis Course about Natural Risks

Effects of the COVID-19 pandemic on the online learning behaviors of university students in Taiwan

Disclosing Personal Names in Screen Names Predicts Better Final Achievement Levels in Massive Open Online Courses

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