An integrated framework for developing and evaluating an automated lecture style assessment system

• URL: http://arxiv.org/abs/2312.00201v2
• Date: Thu, 28 Dec 2023 11:08:54 GMT
• Title: An integrated framework for developing and evaluating an automated lecture style assessment system
• Authors: Eleni Dimitriadou and Andreas Lanitis
• Abstract summary: The proposed application utilizes specific measurable biometric characteristics, such as facial expressions, body activity, speech rate and intonation, hand movement, and facial pose. Results indicate that participants found the application novel and useful in providing automated feedback regarding lecture quality.
• Score: 0.784125444722239
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The aim of the work presented in this paper is to develop and evaluate an integrated system that provides automated lecture style evaluation, allowing teachers to get instant feedback related to the goodness of their lecturing style. The proposed system aims to promote improvement of lecture quality, that could upgrade the overall student learning experience. The proposed application utilizes specific measurable biometric characteristics, such as facial expressions, body activity, speech rate and intonation, hand movement, and facial pose, extracted from a video showing the lecturer from the audience point of view. Measurable biometric features extracted during a lecture are combined to provide teachers with a score reflecting lecture style quality both at frame rate and by providing lecture quality metrics for the whole lecture. The acceptance of the proposed lecture style evaluation system was evaluated by chief education officers, teachers and students regarding the functionality, usefulness of the application, and possible improvements. The results indicate that participants found the application novel and useful in providing automated feedback regarding lecture quality. Furthermore, the performance evaluation of the proposed system was compared with the performance of humans in the task of lecture style evaluation. Results indicate that the proposed system not only achieves similar performance to human observers, but in some cases, it outperforms them.

Related papers

• AERA Chat: An Interactive Platform for Automated Explainable Student Answer Assessment [12.970776782360366]
  AERA Chat is an interactive platform to provide visually explained assessment of student answers. Users can input questions and student answers to obtain automated, explainable assessment results from large language models.
  arXiv  Detail & Related papers  (2024-10-12T11:57:53Z)
• Beyond Thumbs Up/Down: Untangling Challenges of Fine-Grained Feedback for Text-to-Image Generation [67.88747330066049]
  Fine-grained feedback captures nuanced distinctions in image quality and prompt-alignment. We show that demonstrating its superiority to coarse-grained feedback is not automatic. We identify key challenges in eliciting and utilizing fine-grained feedback.
  arXiv  Detail & Related papers  (2024-06-24T17:19:34Z)
• Rethinking the Evaluation of Dialogue Systems: Effects of User Feedback on Crowdworkers and LLMs [57.16442740983528]
  In ad-hoc retrieval, evaluation relies heavily on user actions, including implicit feedback. The role of user feedback in annotators' assessment of turns in a conversational perception has been little studied. We focus on how the evaluation of task-oriented dialogue systems ( TDSs) is affected by considering user feedback, explicit or implicit, as provided through the follow-up utterance of a turn being evaluated.
  arXiv  Detail & Related papers  (2024-04-19T16:45:50Z)
• Automated Assessment of Encouragement and Warmth in Classrooms Leveraging Multimodal Emotional Features and ChatGPT [7.273857543125784]
  Our work explores a multimodal approach to automatically estimating encouragement and warmth in classrooms. We employed facial and speech emotion recognition with sentiment analysis to extract interpretable features from video, audio, and transcript data. We demonstrated our approach on the GTI dataset, comprising 367 16-minute video segments from 92 authentic lesson recordings.
  arXiv  Detail & Related papers  (2024-04-01T16:58:09Z)
• Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels [95.44077384918725]
  We propose to teach large multi-modality models (LMMs) with text-defined rating levels instead of scores. The proposed Q-Align achieves state-of-the-art performance on image quality assessment (IQA), image aesthetic assessment (IAA) and video quality assessment (VQA) tasks.
  arXiv  Detail & Related papers  (2023-12-28T16:10:25Z)
• PapagAI:Automated Feedback for Reflective Essays [48.4434976446053]
  We present the first open-source automated feedback tool based on didactic theory and implemented as a hybrid AI system. The main objective of our work is to enable better learning outcomes for students and to complement the teaching activities of lecturers.
  arXiv  Detail & Related papers  (2023-07-10T11:05:51Z)
• A Multicriteria Evaluation for Data-Driven Programming Feedback Systems: Accuracy, Effectiveness, Fallibility, and Students' Response [7.167352606079407]
  Data-driven programming feedback systems can help novices to program in the absence of a human tutor. Prior evaluations showed that these systems improve learning in terms of test scores, or task completion efficiency. These aspects include inherent fallibility of current state-of-the-art, students' programming behavior in response to correct/incorrect feedback, and effective/ineffective system components.
  arXiv  Detail & Related papers  (2022-07-27T00:29:32Z)
• A literature survey on student feedback assessment tools and their usage in sentiment analysis [0.0]
  We evaluate the effectiveness of various in-class feedback assessment methods such as Kahoot!, Mentimeter, Padlet, and polling. We propose a sentiment analysis model for extracting the explicit suggestions from the students' qualitative feedback comments.
  arXiv  Detail & Related papers  (2021-09-09T06:56:30Z)
• Self-supervised Co-training for Video Representation Learning [103.69904379356413]
  We investigate the benefit of adding semantic-class positives to instance-based Info Noise Contrastive Estimation training. We propose a novel self-supervised co-training scheme to improve the popular infoNCE loss. We evaluate the quality of the learnt representation on two different downstream tasks: action recognition and video retrieval.
  arXiv  Detail & Related papers  (2020-10-19T17:59:01Z)
• PONE: A Novel Automatic Evaluation Metric for Open-Domain Generative Dialogue Systems [48.99561874529323]
  There are three kinds of automatic methods to evaluate the open-domain generative dialogue systems. Due to the lack of systematic comparison, it is not clear which kind of metrics are more effective. We propose a novel and feasible learning-based metric that can significantly improve the correlation with human judgments.
  arXiv  Detail & Related papers  (2020-04-06T04:36:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.