Résumé
Affective computing (AC) has been regarded as a relevant approach to identifying online learners' mental states and predicting their learning performance. Previous research mainly used one single-source data set, typically learners' facial expression, to compute learners' affection. However, a single facial expression may represent different affections in various head poses. This study proposed a dual-source data approach to solve the problem. Facial expression and head pose are two typical data sources that can be captured from online learning videos. The current study collected a dual-source data set of facial expressions and head poses from an online learning class in a middle school. A deep learning neural network using AlexNet with an attention mechanism was developed to verify the syncretic effect on affective computing of the proposed dual-source fusion strategy. The results show that the dual-source fusion approach significantly outperforms the single-source approach based on the AC recognition accuracy between the two approaches (dual-source approach using Attention-AlexNet model 80.96%;single-source approach, facial expression 76.65% and head pose 64.34%). This study contributes to the theoretical construction of the dual-source data fusion approach, and the empirical validation of the effect of the Attention-AlexNet neural network approach on affective computing in online learning contexts.
Résumé
Social networking sites (SNSs) are widely used to support online learning and knowledge exchange (KE) in projects that require the coordination of collaborative team-playing, especially in COVID-19 world pandemic. Paradoxically, while digital infrastructures enable instant communication, SNSs are not always conducive to KE behaviours, as learners are reticent to exchange knowledge with their peers online due to trust issues like personal privacy and Intellectual property rights protection, leads online users to conceal knowledge. Given that online-based KE practices are still in their infancy, one major weakness in current educational and knowledge management research is the lack of a reliable scale to measure knowledge-hiding online. This study develops and validates a novel Online Knowledge-hiding (OKH) scale that keeps into account the distinctive features of SNSs. While building on prior measurements of knowledge-hiding, we identify, test, and validate different indicators of the latent OKH construct. A mixed-method approach to scale development was used to validate this scale. Results show that the proposed scale is effective for organisations to assess whether online knowledge-hiding takes place during KE activities. The usefulness of this scale lies in improving the online collaborative learning environment while setting the foundation to address KE-related deviant behaviours at their incipit. [ABSTRACT FROM AUTHOR] Copyright of Interactive Learning Environments is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)