What Makes a Star Teacher? A Hierarchical BERT Model for Evaluating Teacher's Performance in Online Education

• URL: http://arxiv.org/abs/2012.01633v1
• Date: Thu, 3 Dec 2020 01:51:20 GMT
• Title: What Makes a Star Teacher? A Hierarchical BERT Model for Evaluating Teacher's Performance in Online Education
• Authors: Wen Wang, Honglei Zhuang, Mi Zhou, Hanyu Liu, Beibei Li
• Abstract summary: We conduct a systematic study to understand and effectively predict teachers' performance using the subtitles of 1,085 online courses. Based on these insights, we then propose a hierarchical course BERT model to predict teachers' performance in online education. Our proposed model can capture the hierarchical structure within each course as well as the deep semantic features extracted from the course content.
• Score: 9.849385259376524
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Education has a significant impact on both society and personal life. With the development of technology, online education has been growing rapidly over the past decade. While there are several online education studies on student behavior analysis, the course concept mining, and course recommendations (Feng, Tang, and Liu 2019; Pan et al. 2017), there is little research on evaluating teachers' performance in online education. In this paper, we conduct a systematic study to understand and effectively predict teachers' performance using the subtitles of 1,085 online courses. Our model-free analysis shows that teachers' verbal cues (e.g., question strategy, emotional appealing, and hedging) and their course structure design are both significantly correlated with teachers' performance evaluation. Based on these insights, we then propose a hierarchical course BERT model to predict teachers' performance in online education. Our proposed model can capture the hierarchical structure within each course as well as the deep semantic features extracted from the course content. Experiment results show that our proposed method achieves significant gain over several state-of-the-art methods. Our study provides a significant social impact in helping teachers improve their teaching style and enhance their instructional material design for more effective online teaching in the future.

Related papers

• YODA: Teacher-Student Progressive Learning for Language Models [82.0172215948963]
  This paper introduces YODA, a teacher-student progressive learning framework. It emulates the teacher-student education process to improve the efficacy of model fine-tuning. Experiments show that training LLaMA2 with data from YODA improves SFT with significant performance gain.
  arXiv  Detail & Related papers  (2024-01-28T14:32:15Z)
• DialogID: A Dialogic Instruction Dataset for Improving Teaching Effectiveness in Online Environments [24.094249468028664]
  We present a dataset of online dialogic instruction detection, textscDialogID, which contains 30,431 effective dialogic instructions. We propose a simple yet effective adversarial training learning paradigm to improve the quality and generalization of dialogic instruction detection.
  arXiv  Detail & Related papers  (2022-06-24T02:07:12Z)
• Better Teacher Better Student: Dynamic Prior Knowledge for Knowledge Distillation [70.92135839545314]
  We propose the dynamic prior knowledge (DPK), which integrates part of teacher's features as the prior knowledge before the feature distillation. Our DPK makes the performance of the student model positively correlated with that of the teacher model, which means that we can further boost the accuracy of students by applying larger teachers.
  arXiv  Detail & Related papers  (2022-06-13T11:52:13Z)
• Iterative Teacher-Aware Learning [136.05341445369265]
  In human pedagogy, teachers and students can interact adaptively to maximize communication efficiency. We propose a gradient optimization based teacher-aware learner who can incorporate teacher's cooperative intention into the likelihood function.
  arXiv  Detail & Related papers  (2021-10-01T00:27:47Z)
• The Evaluation Case Study of Online Course During Pandemic Period in Mongolia [0.0]
  This paper describes a test and case study of self-evaluation of online courses during the pandemic time. To sustain the education development teaching methods had to switch from traditional face-to-face teaching to online courses. The focus of this paper is to share the evaluation process of e-learning based on a structure-oriented evaluation model.
  arXiv  Detail & Related papers  (2021-05-26T09:35:40Z)
• Point Adversarial Self Mining: A Simple Method for Facial Expression Recognition [79.75964372862279]
  We propose Point Adversarial Self Mining (PASM) to improve the recognition accuracy in facial expression recognition. PASM uses a point adversarial attack method and a trained teacher network to locate the most informative position related to the target task. The adaptive learning materials generation and teacher/student update can be conducted more than one time, improving the network capability iteratively.
  arXiv  Detail & Related papers  (2020-08-26T06:39:24Z)
• Key principles for workforce upskilling via online learning: a learning analytics study of a professional course in additive manufacturing [2.014343808433054]
  This study combines learning objective analysis and visual learning analytics to examine the relationships among learning trajectories, engagement, and performance. The study also emphasizes broader strategies for course designers and instructors to align course assignments, learning objectives, and assessment measures with learner needs and interests.
  arXiv  Detail & Related papers  (2020-08-15T00:30:56Z)
• Interactive Knowledge Distillation [79.12866404907506]
  We propose an InterActive Knowledge Distillation scheme to leverage the interactive teaching strategy for efficient knowledge distillation. In the distillation process, the interaction between teacher and student networks is implemented by a swapping-in operation. Experiments with typical settings of teacher-student networks demonstrate that the student networks trained by our IAKD achieve better performance than those trained by conventional knowledge distillation methods.
  arXiv  Detail & Related papers  (2020-07-03T03:22:04Z)
• Neural Multi-Task Learning for Teacher Question Detection in Online Classrooms [50.19997675066203]
  We build an end-to-end neural framework that automatically detects questions from teachers' audio recordings. By incorporating multi-task learning techniques, we are able to strengthen the understanding of semantic relations among different types of questions.
  arXiv  Detail & Related papers  (2020-05-16T02:17:04Z)

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.