Visualizing Self-Regulated Learner Profiles in Dashboards: Design Insights from Teachers

• URL: http://arxiv.org/abs/2305.16851v1
• Date: Fri, 26 May 2023 12:03:11 GMT
• Title: Visualizing Self-Regulated Learner Profiles in Dashboards: Design Insights from Teachers
• Authors: Paola Mejia-Domenzain, Eva Laini, Seyed Parsa Neshaei, Thiemo Wambsganss and Tanja K\"aser
• Abstract summary: We design and implement FlippED, a dashboard for monitoring students' self-regulated learning (SRL) behavior. We evaluate the usability and actionability of the tool in semi-structured interviews with ten university teachers.
• Score: 9.227158301570787
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Flipped Classrooms (FC) are a promising teaching strategy, where students engage with the learning material before attending face-to-face sessions. While pre-class activities are critical for course success, many students struggle to engage effectively in them due to inadequate of self-regulated learning (SRL) skills. Thus, tools enabling teachers to monitor students' SRL and provide personalized guidance have the potential to improve learning outcomes. However, existing dashboards mostly focus on aggregated information, disregarding recent work leveraging machine learning (ML) approaches that have identified comprehensive, multi-dimensional SRL behaviors. Unfortunately, the complexity of such findings makes them difficult to communicate and act on. In this paper, we follow a teacher-centered approach to study how to make thorough findings accessible to teachers. We design and implement FlippED, a dashboard for monitoring students' SRL behavior. We evaluate the usability and actionability of the tool in semi-structured interviews with ten university teachers. We find that communicating ML-based profiles spark a range of potential interventions for students and course modifications.

Related papers

• What Makes CLIP More Robust to Long-Tailed Pre-Training Data? A Controlled Study for Transferable Insights [67.72413262980272]
  Severe data imbalance naturally exists among web-scale vision-language datasets. We find CLIP pre-trained thereupon exhibits notable robustness to the data imbalance compared to supervised learning. The robustness and discriminability of CLIP improve with more descriptive language supervision, larger data scale, and broader open-world concepts.
  arXiv  Detail & Related papers  (2024-05-31T17:57:24Z)
• Evaluating and Optimizing Educational Content with Large Language Model Judgments [52.33701672559594]
  We use Language Models (LMs) as educational experts to assess the impact of various instructions on learning outcomes. We introduce an instruction optimization approach in which one LM generates instructional materials using the judgments of another LM as a reward function. Human teachers' evaluations of these LM-generated worksheets show a significant alignment between the LM judgments and human teacher preferences.
  arXiv  Detail & Related papers  (2024-03-05T09:09:15Z)
• Impact of Guidance and Interaction Strategies for LLM Use on Learner Performance and Perception [19.335003380399527]
  Large language models (LLMs) offer a promising avenue, with increasing research exploring their educational utility. Our work highlights the role that teachers can play in shaping LLM-supported learning environments.
  arXiv  Detail & Related papers  (2023-10-13T01:21:52Z)
• Empowering Private Tutoring by Chaining Large Language Models [87.76985829144834]
  This work explores the development of a full-fledged intelligent tutoring system powered by state-of-the-art large language models (LLMs) The system is into three inter-connected core processes-interaction, reflection, and reaction. Each process is implemented by chaining LLM-powered tools along with dynamically updated memory modules.
  arXiv  Detail & Related papers  (2023-09-15T02:42:03Z)
• Responsible Active Learning via Human-in-the-loop Peer Study [88.01358655203441]
  We propose a responsible active learning method, namely Peer Study Learning (PSL), to simultaneously preserve data privacy and improve model stability. We first introduce a human-in-the-loop teacher-student architecture to isolate unlabelled data from the task learner (teacher) on the cloud-side. During training, the task learner instructs the light-weight active learner which then provides feedback on the active sampling criterion.
  arXiv  Detail & Related papers  (2022-11-24T13:18:27Z)
• A Machine Learning system to monitor student progress in educational institutes [0.0]
  We propose a data driven approach that makes use of Machine Learning techniques to generate a classifier called credit score. The proposal to use credit score as progress indicator is well suited to be used in a Learning Management System.
  arXiv  Detail & Related papers  (2022-11-02T08:24:08Z)
• Implicit Offline Reinforcement Learning via Supervised Learning [83.8241505499762]
  Offline Reinforcement Learning (RL) via Supervised Learning is a simple and effective way to learn robotic skills from a dataset collected by policies of different expertise levels. We show how implicit models can leverage return information and match or outperform explicit algorithms to acquire robotic skills from fixed datasets.
  arXiv  Detail & Related papers  (2022-10-21T21:59:42Z)
• Demonstrating REACT: a Real-time Educational AI-powered Classroom Tool [0.9899017174990579]
  We present a new Real-time Educational AI-powered Classroom Tool that employs EDM techniques for supporting the decision-making process of educators. ReACT is a data-driven tool with a user-friendly graphical interface. It analyzes students' performance data and provides context-based alerts as well as recommendations to educators for course planning.
  arXiv  Detail & Related papers  (2021-07-30T03:09:59Z)
• The Wits Intelligent Teaching System: Detecting Student Engagement During Lectures Using Convolutional Neural Networks [0.30458514384586394]
  The Wits Intelligent Teaching System (WITS) aims to assist lecturers with real-time feedback regarding student affect. A CNN based on AlexNet is successfully trained and which significantly outperforms a Support Vector Machine approach.
  arXiv  Detail & Related papers  (2021-05-28T12:59:37Z)
• MOLAM: A Mobile Multimodal Learning Analytics Conceptual Framework to Support Student Self-Regulated Learning [0.0]
  This chapter introduces a Mobile Multimodal Learning Analytics approach (MOLAM) I argue that the development of student Self-Regulated Learning would benefit from the adoption of this approach.
  arXiv  Detail & Related papers  (2020-12-18T18:55:33Z)
• Interaction-limited Inverse Reinforcement Learning [50.201765937436654]
  We present two different training strategies: Curriculum Inverse Reinforcement Learning (CIRL) covering the teacher's perspective, and Self-Paced Inverse Reinforcement Learning (SPIRL) focusing on the learner's perspective. Using experiments in simulations and experiments with a real robot learning a task from a human demonstrator, we show that our training strategies can allow a faster training than a random teacher for CIRL and than a batch learner for SPIRL.
  arXiv  Detail & Related papers  (2020-07-01T12:31:52Z)

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.