Time allocated for lecturing and student discussions is an important indicator of classroom quality assessment. Automated classification of lecture and discussion recording segments can serve as an indicator of classroom activity in a flipped classroom setting. Segments of lecture are primarily the speech of the lecturer, while segments of discussion include student speech, silence and noise. Multiple audio recorders simultaneously document all class activities. Recordings are coarsely synchronized to a common start time. We note that the lecturer's speech tends to be common across recordings, but student discussions are captured only in the nearby device(s). Therefore, we window each recording at 0.5 s to 5 s duration and 0.1 s analysis rate. We compute the normalized similarity between a given window and temporally proximate window segments in other recordings. Histogram plot categorizes higher similarity windows as lecture and lower ones as discussion. To improve the classification performance, high energy lecture windows and windows with very high and very low similarity are used to train a supervised model, in order to regenerate the classification results of remaining windows. Experimental results show that binary classification accuracy improves from 96.84% to 97.37%.
The flipped classroom is a new pedagogical strategy that has been gaining increasing importance recently. Spoken discussion dialog commonly occurs in flipped classroom, which embeds rich information indicating processes and progression of students' learning. This study focuses on learning analytics from spoken discussion dialog in the flipped classroom, which aims to collect and analyze the discussion dialogs in flipped classroom in order to get to know group learning processes and outcomes. We have recently transformed a course using the flipped classroom strategy, where students watched video-recorded lectures at home prior to group-based problem-solving discussions in class. The in-class group discussions were recorded throughout the semester and then transcribed manually. After features are extracted from the dialogs by multiple tools and customized processing techniques, we performed statistical analyses to explore the indicators that are related to the group learning outcomes from face-to-face discussion dialogs in the flipped classroom. Then, machine learning algorithms are applied to the indicators in order to predict the group learning outcome as High, Mid or Low. The best prediction accuracy reaches 78.9%, which demonstrates the feasibility of achieving automatic learning outcome prediction from group discussion dialog in flipped classroom.
Dialog systems enriched with external knowledge can handle user queries that are outside the scope of the supporting databases/APIs. In this paper, we follow the baseline provided in DSTC9 Track 1 and propose three subsystems, KDEAK, KnowleDgEFactor, and Ens-GPT, which form the pipeline for a task-oriented dialog system capable of accessing unstructured knowledge. Specifically, KDEAK performs knowledge-seeking turn detection by formulating the problem as natural language inference using knowledge from dialogs, databases and FAQs. KnowleDgEFactor accomplishes the knowledge selection task by formulating a factorized knowledge/document retrieval problem with three modules performing domain, entity and knowledge level analyses. Ens-GPT generates a response by first processing multiple knowledge snippets, followed by an ensemble algorithm that decides if the response should be solely derived from a GPT2-XL model, or regenerated in combination with the top-ranking knowledge snippet. Experimental results demonstrate that the proposed pipeline system outperforms the baseline and generates high-quality responses, achieving at least 58.77% improvement on BLEU-4 score.
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