Identifying rote learning and the supporting effects of hints in drills

• URL: http://arxiv.org/abs/2108.12288v1
• Date: Thu, 19 Aug 2021 16:29:50 GMT
• Title: Identifying rote learning and the supporting effects of hints in drills
• Authors: Gunnar Stefansson, Anna Helga Jonsdottir, Thorarinn Jonmundsson, Gylfi Snaer Sigurdsson, Ingunn Lilja Bergsdottir
• Abstract summary: The tutor-web is an online drilling system. The design aim of the system is learning rather than evaluation. The analyses show non-rote learning, but even with large question databases, students' performance is better when they are presented with an answer option they have seen before.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Whenever students use any drilling system the question arises how much of their learning is meaningful learning vs memorisation through repetition or rote learning. Although both types of learning have their place in an educational system it is important to be able to distinguish between these two approaches to learning and identify options which can dislodge students from rote learning and motivate them towards meaningful learning. The tutor-web is an online drilling system. The design aim of the system is learning rather than evaluation. This is done by presenting students with multiple-choice questions which are selected randomly but linked to the students' performance. The questions themselves can be generated for a specific topic by drawing correct and incorrect answers from a collection associated with a general problem statement or heading. With this generating process students may see the same question heading twice but be presented with all new answer options or a mixture of new and old answer options. Data from a course on probability theory and statistics, taught during COVID-19, are analysed to separate rote learning from meaningful learning. The analyses show non-rote learning, but even with large question databases, students' performance is better when they are presented with an answer option they have seen before. An element of rote learning is thus exhibited but a deeper learning is also demonstrated. The item database has been seeded with hints such that some questions contain clues to cue the students towards the correct answer. This ties in with the issue of meaningful learning versus rote learning since the hope is that a new hint will work as a cue to coax the student to think harder about the question rather than continue to employ rote learning. Preliminary results indicate that hints are particularly useful for students with poor performance metrics.

Related papers

• How Do Students Interact with an LLM-powered Virtual Teaching Assistant in Different Educational Settings? [3.9134031118910264]
  Jill Watson, a virtual teaching assistant powered by LLMs, answers student questions and engages them in extended conversations on courseware provided by the instructors. In this paper, we analyze student interactions with Jill across multiple courses and colleges. We find that, by supporting a wide range of cognitive demands, Jill encourages students to engage in sophisticated, higher-order cognitive questions.
  arXiv  Detail & Related papers  (2024-07-15T01:22:50Z)
• 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)
• Ticketed Learning-Unlearning Schemes [57.89421552780526]
  We propose a new ticketed model for learning--unlearning. We provide space-efficient ticketed learning--unlearning schemes for a broad family of concept classes.
  arXiv  Detail & Related papers  (2023-06-27T18:54:40Z)
• UNIKD: UNcertainty-filtered Incremental Knowledge Distillation for Neural Implicit Representation [48.49860868061573]
  Recent neural implicit representations (NIRs) have achieved great success in the tasks of 3D reconstruction and novel view synthesis. They require the images of a scene from different camera views to be available for one-time training. This is expensive especially for scenarios with large-scale scenes and limited data storage. We design a student-teacher framework to mitigate the catastrophic problem.
  arXiv  Detail & Related papers  (2022-12-21T11:43:20Z)
• Teacher-student curriculum learning for reinforcement learning [1.7259824817932292]
  Reinforcement learning (rl) is a popular paradigm for sequential decision making problems. The sample inefficiency of deep reinforcement learning methods is a significant obstacle when applying rl to real-world problems. We propose a teacher-student curriculum learning setting where we simultaneously train a teacher that selects tasks for the student while the student learns how to solve the selected task.
  arXiv  Detail & Related papers  (2022-10-31T14:45:39Z)
• Know Thy Student: Interactive Learning with Gaussian Processes [11.641731210416102]
  Our work proposes a simple diagnosis algorithm which uses Gaussian processes for inferring student-related information, before constructing a teaching dataset. We study this in the offline reinforcement learning setting where the teacher must provide demonstrations to the student and avoid sending redundant trajectories. Our experiments highlight the importance of diagosing before teaching and demonstrate how students can learn more efficiently with the help of an interactive teacher.
  arXiv  Detail & Related papers  (2022-04-26T04:43:57Z)
• On the Efficiency of Integrating Self-supervised Learning and Meta-learning for User-defined Few-shot Keyword Spotting [51.41426141283203]
  User-defined keyword spotting is a task to detect new spoken terms defined by users. Previous works try to incorporate self-supervised learning models or apply meta-learning algorithms. Our result shows that HuBERT combined with Matching network achieves the best result.
  arXiv  Detail & Related papers  (2022-04-01T10:59:39Z)
• Real-Time Cognitive Evaluation of Online Learners through Automatically Generated Questions [0.0]
  The paper presents an approach to generate questions from a given video lecture automatically. The generated questions are aimed to evaluate learners' lower-level cognitive abilities.
  arXiv  Detail & Related papers  (2021-06-06T05:45:56Z)
• Exploring Bayesian Deep Learning for Urgent Instructor Intervention Need in MOOC Forums [58.221459787471254]
  Massive Open Online Courses (MOOCs) have become a popular choice for e-learning thanks to their great flexibility. Due to large numbers of learners and their diverse backgrounds, it is taxing to offer real-time support. With the large volume of posts and high workloads for MOOC instructors, it is unlikely that the instructors can identify all learners requiring intervention. This paper explores for the first time Bayesian deep learning on learner-based text posts with two methods: Monte Carlo Dropout and Variational Inference.
  arXiv  Detail & Related papers  (2021-04-26T15:12:13Z)
• 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)
• Choose Your Own Question: Encouraging Self-Personalization in Learning Path Construction [1.6505359493498744]
  We introduce Rocket, a Tinder-like User Interface for a general class of Interactive Educational System (IES)s. Rocket provides a visual representation of Artificial Intelligence (AI)-extracted features of learning materials, allowing the student to quickly decide whether the material meets their needs. Rocket enables self-personalization of the learning experience by leveraging the students' knowledge of their own abilities and needs.
  arXiv  Detail & Related papers  (2020-05-08T01:53:04Z)

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.