Automatic content analysis of asynchronous discussion forum transcripts: A systematic literature review

Ahmad, Mubarik; Junus, Kasiyah; Santoso, Harry Budi

doi:10.1007/s10639-022-11065-w

Cited by 15 publications

(8 citation statements)

References 92 publications

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“…Third, the automated group learning engagement analysis and feedback approach is efficient for providing real-time analysis results and group-specific feedback with the aid of text mining techniques, especially using deep neural network models. Text mining techniques play a leading role in automatically analyzing online discussion transcripts (Ahmad et al, 2022). As a deep learning technique, deep neural network models are inherently able to overcome overfitting and the disadvantages of traditional machine learning algorithms dependent on hand-designed features (Liu et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

An automated group learning engagement analysis and feedback approach to promoting collaborative knowledge building, group performance, and socially shared regulation in CSCL

Zheng

Long

Niu

et al. 2023

Intern. J. Comput.-Support. Collab. Learn

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Learning engagement has gained increasing attention in the field of education. Previous studies have adopted conventional methods to analyze learning engagement, but these methods cannot provide timely feedback for learners. This study analyzed automated group learning engagement via deep neural network models in a computer-supported collaborative learning (CSCL) context. A quasi-experimental research design was implemented to examine the effects of the automated group learning engagement analysis and feedback approach on collaborative knowledge building, group performance, socially shared regulation, and cognitive load. In total, 120 college students participated in this study; they were assigned to 20 experimental groups and 20 control groups of three students each. The students in the experimental groups adopted the automated group learning engagement analysis and feedback approach, whereas those in the control groups used the traditional online collaborative learning approach. Both quantitative and qualitative data were collected and analyzed in depth. The results indicated significant differences in group learning engagement, group performance, collaborative knowledge building, and socially shared regulation between the experimental and control groups. The proposed approach did not increase the cognitive load for the experimental groups. The implications of the findings can potentially contribute to improving group learning engagement and group performance in CSCL.

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Section: Discussionmentioning

confidence: 99%

An automated group learning engagement analysis and feedback approach to promoting collaborative knowledge building, group performance, and socially shared regulation in CSCL

Zheng

Long

Niu

et al. 2023

Intern. J. Comput.-Support. Collab. Learn

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“…Video-driven discussions motivate students to ponder the content offered in the videos and activate their deeper cognitive processes. (Ahmad et al, 2022;Giacumo & Savenye, 2020). Moreover, in these online environments, students can express themselves better and have opportunities to evaluate themselves indirectly by examining the perspectives of their friends.…”

Section: Discussionmentioning

confidence: 99%

The effects of video‐driven discussions integrated into the flipped classroom model on learning achievement, practical performance, and higher‐order thinking skills in dental education

Fidan,

Fidan

2023

Computer Assisted Learning

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BackgroundFlipped classroom (FC) model has become increasingly popular in dental education (DE) with its strengths for students. However, major concerns are lack of interaction, unwillingness to complete the assignments, low engagement during the pre‐class activities because of an unwell‐designed instructional setting of FC.ObjectivesThe recent study aims to investigate the effects of video‐driven discussions integrated into the flipped classroom (VdDs‐FC) model on dental students' (DSs) learning achievement, practical performance, and higher‐order thinking skills in a restorative dentistry course.MethodsThis study adopted a sequential explanatory design from mixed‐methods research. A total of 96 DSs participated in a dentistry faculty of Türkiye. They were randomly divided into two groups: VdDs‐FC group and the conventional flipped classroom (c‐FC) group.Results and conclusionsThe results showed that the VdDs‐FC model had significant positive effects on the practical performance of DSs in practical subjects and their problem‐solving and critical thinking skills. However, there was no significant difference between the two groups in learning achievement and creativity. The use of FC in the c‐FC group did not lead to any difference in the students' achievements. Moreover, the qualitative results indicated that the VdDs‐FC model has the potential for enhancing peer learning, practical classroom activities, and higher‐order thinking skills.ImplicationsThis study also includes several instructional implications and suggestions for educators, researchers, and practitioners on use of FC in dentistry.

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“…The same progression from traditional (shallow) machine learning methods to deep learning has taken place in the domain of forum post classification (Ahmad et al, 2022). Since the present work explores three distinct classification tasks (category, structure, and emotion), the use of transformer-based models, and strategies for reducing human annotation burden, we highlight relevant work in these areas.…”

Section: Automated Forum Post Classificationmentioning

confidence: 96%

AI-assisted analysis of content, structure, and sentiment in MOOC discussion forums

Yee,

Roy,

Perdue

et al. 2023

Front. Educ.

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Discussion forums are a key component of online learning platforms, allowing learners to ask for help, provide help to others, and connect with others in the learning community. Analyzing patterns of forum usage and their association with course outcomes can provide valuable insight into how learners actually use discussion forums, and suggest strategies for shaping forum dynamics to improve learner experiences and outcomes. However, the fine-grained coding of forum posts required for this kind of analysis is a manually intensive process that can be challenging for large datasets, e.g., those that result from popular MOOCs. To address this issue, we propose an AI-assisted labeling process that uses advanced natural language processing techniques to train machine learning models capable of labeling a large dataset while minimizing human annotation effort. We fine-tune pretrained transformer-based deep learning models on category, structure, and emotion classification tasks. The transformer-based models outperform a more traditional baseline that uses support vector machines and a bag-of-words input representation. The transformer-based models also perform better when we augment the input features for an individual post with additional context from the post's thread (e.g., the thread title). We validate model quality through a combination of internal performance metrics, human auditing, and common-sense checks. For our Python MOOC dataset, we find that annotating approximately 1% of the forum posts achieves performance levels that are reliable for downstream analysis. Using labels from the validated AI models, we investigate the association of learner and course attributes with thread resolution and various forms of forum participation. We find significant differences in how learners of different age groups, gender, and course outcome status ask for help, provide help, and make posts with emotional (positive or negative) sentiment.

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Automatic content analysis of asynchronous discussion forum transcripts: A systematic literature review

Cited by 15 publications

References 92 publications

An automated group learning engagement analysis and feedback approach to promoting collaborative knowledge building, group performance, and socially shared regulation in CSCL

An automated group learning engagement analysis and feedback approach to promoting collaborative knowledge building, group performance, and socially shared regulation in CSCL

The effects of video‐driven discussions integrated into the flipped classroom model on learning achievement, practical performance, and higher‐order thinking skills in dental education

AI-assisted analysis of content, structure, and sentiment in MOOC discussion forums

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