Towards better understanding of hot topics in online learning communities

Li, Yanyan; Zheng, Yafeng; Bao, Haogang; Liu, Yang

doi:10.1186/s40561-015-0019-6

Cited by 3 publications

(3 citation statements)

References 20 publications

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“…Many studies have adopted traditional methods to detect topics in the field of online learning. For example, Li et al ( 2015 ) employed key terms to detect hot topics in online learning communities. Chen et al ( 2021 ) adopted Latent Dirichlet Allocation (LDA) to detect topics in asynchronous online discussion to help learners grasp discussion topics.…”

Section: Literature Reviewmentioning

confidence: 99%

An immediate analysis of the interaction topic approach to promoting group performance, knowledge convergence, cognitive engagement, and coregulation in online collaborative learning

2023

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Online collaborative learning (OCL) has been a mainstream pedagogy in the field of higher education. However, learners often produce off-topic information and engage less during online collaborative learning compared to other approaches. In addition, learners often cannot converge in knowledge, and they often do not know how to coregulate with peers. To cope with these problems, this study proposed an immediate analysis of interaction topics (IAIT) approach through deep learning technologies. The purpose of this study is to examine the effects of the IAIT approach on group performance, knowledge convergence, coregulation, and cognitive engagement in online collaborative learning. In total, 60 undergraduate students participated in this quasi-experimental study. They were assigned to either the experimental or the control groups. The students in the experimental groups conducted online collaborative learning with the IAIT approach, and the students in the control groups conducted online collaborative learning only without any particular approach. The whole study lasted for three months. Both qualitative and quantitative methods were adopted to analyze data. The results indicated that the IAIT approach significantly promoted group performance, knowledge convergence, coregulated behaviors, and cognitive engagement. The IAIT approach did not increase learners’ cognitive load. The results, together with the implications for teachers, practitioners and researchers, are also discussed in depth.

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Section: Literature Reviewmentioning

confidence: 99%

An immediate analysis of the interaction topic approach to promoting group performance, knowledge convergence, cognitive engagement, and coregulation in online collaborative learning

2023

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“…Communities of learners 9 (13.23%) [28], [41], [62], [67], [69], [72], [73], [81], [84] Competency-based education 9 (13.23%) [21]- [23], [25], [38], [46], [51], [67], [83] Problem-based learning 8 (11.76%) [26], [40], [47], [48], [73], [75], [76], [82] Project-based learning 8 (11.76%) [23], [35], [47], [59], [67], [76], [82], [84] Active learning 7 (10.29%) [28], [38], [42], [46], [57], [62], [65] Exploratory and discovery learning 6 (8.82%) [28], [41], [43], [46], [47], [67] Simulation-based learning 6 (8.82%) [26], [31],…”

Section: Pedagogical Approaches # Papers (%) Articlesmentioning

confidence: 99%

Affordances and Core Functions of Smart Learning Environments: A Systematic Literature Review

Tabuenca

Serrano-Iglesias

Martín

et al. 2021

IEEE Trans. Learning Technol.

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Smart learning environments (SLEs) have gained considerable momentum in the last 20 years. The term SLE has emerged to encompass a set of recent trends in the field of educational technology, heavily influenced by the growing impact of technologies such as cloud services, mobile devices, and interconnected objects. However, the term SLE has been used inconsistently by the technology-enhanced learning (TEL) community, since different research works employ the adjective "smart" to refer to different aspects of novel learning environments. Previous surveys on SLEs are narrowly focused on specific technologies, or remain at a theoretical level that does not discuss practical implications found in empirical studies. To address this inconsistency, and also to contribute to a common understanding of the SLE concept, this paper presents a systematic literature review (SLR) of papers published between 2000 and 2019 discussing SLEs in empirical studies. Sixty eight papers out of an initial list of 1,341 papers were analyzed to identify: 1) what affordances make a learning environment smart; 2) which technologies are used in SLEs; and 3) in what pedagogical contexts are SLEs used. Considering the limitations of previous surveys, and the inconsistent use of the SLE concept in the TEL community, this paper presents a comprehensive characterization Manuscript sent for review July 30, 2020. Revised month day, year; Accepted month day, year. Date of publication month day, year.

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“…Numerous approaches have been proposed in the literature for generating probability and uncertainty maps (Li 2018, McCrindle et al 2021, Seoni et al 2023. Monte Carlo (MC) dropout suggested in 2015 by Gal and Ghahramani (2015) represents one of the most widely adopted techniques due to its straightforward implementation (Bhat et al 2022).…”

Section: Introductionmentioning

confidence: 99%

Clinical assessment of deep learning-based uncertainty maps in lung cancer segmentation

Maruccio,

Eppinga,

Laves

et al. 2024

Phys. Med. Biol.

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Objective. Prior to radiation therapy planning, accurate delineation of gross tumour volume (GTVs) and organs at risk (OARs) is crucial. In the current clinical practice, tumour delineation is performed manually by radiation oncologists, which is time-consuming and prone to large inter-observer variability. With the advent of deep learning (DL) models, automated contouring has become possible, speeding up procedures and assisting clinicians. However, these tools are currently used in the clinic mostly for contouring OARs, since these systems are not reliable yet for contouring GTVs. To improve the reliability of these systems, researchers have started exploring the topic of probabilistic neural networks. However, there is still limited knowledge of the practical implementation of such networks in real clinical settings. Approach. In this work, we developed a 3D probabilistic system that generates DL-based uncertainty maps for lung cancer CT segmentations. We employed the Monte Carlo (MC) dropout technique to generate probabilistic and uncertainty maps, while the model calibration was evaluated by using reliability diagrams. A clinical validation was conducted in collaboration with a radiation oncologist to qualitatively assess the value of the uncertainty estimates. We also proposed two novel metrics, namely mean uncertainty (MU) and relative uncertainty volume (RUV), as potential indicators for clinicians to assess the need for independent visual checks of the DL-based segmentation. Main results. Our study showed that uncertainty mapping effectively identified cases of under or over-contouring. Although the overconfidence of the model, a strong correlation was observed between the clinical opinion and MU metric. Moreover, both MU and RUV revealed high AUC values in discretising between low and high uncertainty cases. Significance. Our study is one of the first attempts to clinically validate uncertainty estimates in DL-based contouring. The two proposed metrics exhibited promising potential as indicators for clinicians to independently assess the quality of tumour delineation.

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Towards better understanding of hot topics in online learning communities

Cited by 3 publications

References 20 publications

An immediate analysis of the interaction topic approach to promoting group performance, knowledge convergence, cognitive engagement, and coregulation in online collaborative learning

An immediate analysis of the interaction topic approach to promoting group performance, knowledge convergence, cognitive engagement, and coregulation in online collaborative learning

Affordances and Core Functions of Smart Learning Environments: A Systematic Literature Review

Clinical assessment of deep learning-based uncertainty maps in lung cancer segmentation

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