Virtual learning communities (VLCs) rethinking: Collaboration between learning communities

Nikiforos, Stefanos; Tzanavaris, Spyros; Kermanidis, Katia Lida

doi:10.1007/s10639-020-10132-4

Cited by 17 publications

(17 citation statements)

References 40 publications

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“…In massive open online courses (MOOCs), NLP techniques along with classification algorithms (e.g., logistic regression, random forest) have examined data from discussion forums for a wide range of tasks such as predicting students' learning outcomes, sentiment analysis [27], confusion detection [14], and cognitive presence [3,12]. NLP techniques, such as LSA and other machine learning techniques have also been applied to group discourse in collaborative learning environments, particularly in CSCL systems [5,7,27,35,47].…”

Section: Natural Language Processing In Learning Analyticsmentioning

confidence: 99%

“…At times, students' talk can cause disruptions that impede collaborative learning processes. Recent work investigating bullying and off-task behavior in collaborative learning environments used word representation approaches along with classic machine learning and deep learning techniques [5,35]. Nikiforos et al investigated the automatic detection of aggressive actions within student dialogues in CSCL environments.…”

Section: Natural Language Processing In Automatic Analysis Of Talk In Collaborative Learning Environmentsmentioning

confidence: 99%

“…Nikiforos et al investigated the automatic detection of aggressive actions within student dialogues in CSCL environments. They adopted unigrams as the word-representation approach and investigated machine learning techniques including naïve Bayes, decision trees, and feedforward neural networks [35]. However, they did not investigate recent word embedding techniques, and they focused on individual messages without considering the dialogue context.…”

Section: Natural Language Processing In Automatic Analysis Of Talk In Collaborative Learning Environmentsmentioning

confidence: 99%

“…Incorporating peer assessments during the CSCL process is an attractive non-computational option for reducing students' disruptive talk as it can encourage positive attitudes toward collaborative problem solving [39]. Previous work has investigated computational approaches for automatically detecting bullying and off-task behavior in collaborative learning environments with language models ranging from classic approaches (e.g., n-grams) to more recent stateof-the-art contextualized word embeddings (e.g., BERT), which were used in conjunction with machine learning classification techniques (e.g., logistic regression, random forest, long-short term memory networks) [5,35].…”

Section: Introductionmentioning

confidence: 99%

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Detecting Disruptive Talk in Student Chat-Based Discussion within Collaborative Game-Based Learning Environments

Park

Sohn

Mott

et al. 2021

LAK21: 11th International Learning Analytics and Knowledge Conference

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Collaborative game-based learning environments offer significant promise for creating engaging group learning experiences. Online chat plays a pivotal role in these environments by providing students with a means to freely communicate during problem solving. These chat-based discussions and negotiations support the coordination of students' in-game learning activities. However, this freedom of expression comes with the possibility that some students might engage in undesirable communicative behavior. A key challenge posed by collaborative game-based learning environments is how to reliably detect disruptive talk that purposefully disrupt team dynamics and problem-solving interactions. Detecting disruptive talk during collaborative game-based learning is particularly important because if it is allowed to persist, it can generate frustration and significantly impede the learning process for students. This paper analyzes disruptive talk in a collaborative game-based learning environment for middle school science education to investigate how such behaviors influence students' learning outcomes and varies across gender and students' prior knowledge. We present a disruptive talk detection framework that automatically detects disruptive talk in chat-based group conversations. We further investigate both classic machine learning and deep learning models for the framework utilizing a range of dialogue representations as well as supplementary information such as student gender. Findings show that long short-term memory network (LSTM)-based disruptive talk detection models outperform competitive baseline models, indicating that the LSTM-based disruptive talk detection framework

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Section: Natural Language Processing In Learning Analyticsmentioning

confidence: 99%

Section: Natural Language Processing In Automatic Analysis Of Talk In Collaborative Learning Environmentsmentioning

confidence: 99%

Section: Natural Language Processing In Automatic Analysis Of Talk In Collaborative Learning Environmentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Detecting Disruptive Talk in Student Chat-Based Discussion within Collaborative Game-Based Learning Environments

Park

Sohn

Mott

et al. 2021

LAK21: 11th International Learning Analytics and Knowledge Conference

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show abstract

“…The COVID-19 pandemic has pushed the educational institutions to provide online services including lectures and student assessment. In this transition, it is very important to record the perspective and attitude of the students towards virtual learning [10].…”

Section: Introductionmentioning

confidence: 99%

Peer Interaction Teaching-Learning Approaches for Effective Engagement of Students in Virtual Classroom

Rajalingam

Kanagamalliga

Karuppiah³

et al. 2021

JEET

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The virtual classroom teaching-learning, online meetings, and discussions are becoming most popular throughout the world due to the COVID-19 pandemic event. There is a need to enhance the engagement and thinking ability of the students during a virtual classroom using suitable tools. This study explores the perspective of engineering students and staff members towards virtual classroom teaching-learning in India and Ghana-West Africa. This study also proposes teaching-learning approaches to motivate students for effective engagement and thinking. As an outcome, problem-solving skills and necessary graduate attributes of the students will be developed and accomplished. The participants involved in the survey include 194 students and staff members from engineering colleges and universities in India and Ghana-West Africa. The questionnaire-based research method is used to collect the perspective of students and staff. The disciplines in engineering such as Electrical and Electronics Engineering, Mechanical Engineering, Civil Engineering, and Computer Engineering are considered for analysis. The survey gives the challenges and benefits of virtual learning faced by the students and staff members. It also gives the expectation of the students and staff doing a virtual meeting. The research findings show that peer interaction teaching-learning methodologies such as brainstorming, role plays, group discussion, case studies, animated videos, games, and activities are best suitable in a virtual classroom to overcome the challenges addressed by the students and staff members. Based on the analysis of the survey, ICTbased tools, and applications for incorporating interactive teaching-learning methodologies have been listed with strategies and approaches to motivate and engage the students effectively in a virtual classroom environment.

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Graphical Representation of the Movements of Motives of Consciousness in Leontyev’s Activity Theory

Tzanavaris

2021

Integr. psych. behav.

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Virtual learning communities (VLCs) rethinking: Collaboration between learning communities

Cited by 17 publications

References 40 publications

Detecting Disruptive Talk in Student Chat-Based Discussion within Collaborative Game-Based Learning Environments

Detecting Disruptive Talk in Student Chat-Based Discussion within Collaborative Game-Based Learning Environments

Peer Interaction Teaching-Learning Approaches for Effective Engagement of Students in Virtual Classroom

Graphical Representation of the Movements of Motives of Consciousness in Leontyev’s Activity Theory

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