Integrating Competency-Based Education in Interactive Learning Systems
- URL: http://arxiv.org/abs/2309.12343v1
- Date: Fri, 25 Aug 2023 15:11:53 GMT
- Title: Integrating Competency-Based Education in Interactive Learning Systems
- Authors: Maximilian S\"olch, Moritz Aberle, Stephan Krusche
- Abstract summary: This paper describes how to make Artemis capable of competency-based education.
We show how instructors can define relations between competencies to create a competency relation graph.
We present the results of a user study regarding the usability of the newly designed competency visualization.
- Score: 1.0052074659955383
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Artemis is an interactive learning system that organizes courses, hosts
lecture content and interactive exercises, conducts exams, and creates
automatic assessments with individual feedback. Research shows that students
have unique capabilities, previous experiences, and expectations. However, the
course content on current learning systems, including Artemis, is not tailored
to a student's competencies. The main goal of this paper is to describe how to
make Artemis capable of competency-based education and provide individual
course content based on the unique characteristics of every student. We show
how instructors can define relations between competencies to create a
competency relation graph, how Artemis measures and visualizes the student's
progress toward mastering a competency, and how the progress can generate a
personalized learning path for students that recommends relevant learning
resources. Finally, we present the results of a user study regarding the
usability of the newly designed competency visualization and give an outlook on
possible improvements and future visions.
Related papers
- Representational Alignment Supports Effective Machine Teaching [81.19197059407121]
We integrate insights from machine teaching and pragmatic communication with the literature on representational alignment.
We design a supervised learning environment that disentangles representational alignment from teacher accuracy.
arXiv Detail & Related papers (2024-06-06T17:48:24Z) - Personalization, Cognition, and Gamification-based Programming Language
Learning: A State-of-the-Art Systematic Literature Review [0.13053649021965597]
Programming courses in computing science are important because they are often the first introduction to computer programming for many students.
The current teacher-lecturer model of learning commonly employed in university lecture halls often results in a lack of motivation and participation in learning.
This paper provides insights into designing and implementing effective personalized gamification interventions in programming courses.
arXiv Detail & Related papers (2023-09-05T05:14:23Z) - Quiz-based Knowledge Tracing [61.9152637457605]
Knowledge tracing aims to assess individuals' evolving knowledge states according to their learning interactions.
QKT achieves state-of-the-art performance compared to existing methods.
arXiv Detail & Related papers (2023-04-05T12:48:42Z) - A Domain-Agnostic Approach for Characterization of Lifelong Learning
Systems [128.63953314853327]
"Lifelong Learning" systems are capable of 1) Continuous Learning, 2) Transfer and Adaptation, and 3) Scalability.
We show that this suite of metrics can inform the development of varied and complex Lifelong Learning systems.
arXiv Detail & Related papers (2023-01-18T21:58:54Z) - 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) - Learning Knowledge Representation with Meta Knowledge Distillation for
Single Image Super-Resolution [82.89021683451432]
We propose a model-agnostic meta knowledge distillation method under the teacher-student architecture for the single image super-resolution task.
Experiments conducted on various single image super-resolution datasets demonstrate that our proposed method outperforms existing defined knowledge representation related distillation methods.
arXiv Detail & Related papers (2022-07-18T02:41:04Z) - Dynamic Diagnosis of the Progress and Shortcomings of Student Learning
using Machine Learning based on Cognitive, Social, and Emotional Features [0.06999740786886534]
Student diversity can be challenging as it adds variability in the way in which students learn and progress over time.
A single teaching approach is likely to be ineffective and result in students not meeting their potential.
This paper discusses a novel methodology based on data analytics and Machine Learning to measure and causally diagnose the progress and shortcomings of student learning.
arXiv Detail & Related papers (2022-04-13T21:14:58Z) - A Network Science Perspective to Personalized Learning [0.0]
We examine how learning objectives can be achieved through a learning platform that offers content choices and multiple modalities of engagement to support self-paced learning.
This framework brings the attention to learning experiences, rather than teaching experiences, by providing the learner engagement and content choices supported by a network of knowledge.
arXiv Detail & Related papers (2021-11-02T01:50:01Z) - Unsupervised Representations Predict Popularity of Peer-Shared Artifacts
in an Online Learning Environment [4.438259529250529]
We represent student artifacts by their (a) contextual action logs (b) textual content, and (c) set of instructor-specified features.
We find that the neural embedding representation, learned from contextual action logs, has the strongest predictions of popularity.
Because this representation can be learnt without extensive human labeling effort, it opens up possibilities for shaping more inclusive student interactions.
arXiv Detail & Related papers (2021-02-27T09:13:09Z) - Personalized Education in the AI Era: What to Expect Next? [76.37000521334585]
The objective of personalized learning is to design an effective knowledge acquisition track that matches the learner's strengths and bypasses her weaknesses to meet her desired goal.
In recent years, the boost of artificial intelligence (AI) and machine learning (ML) has unfolded novel perspectives to enhance personalized education.
arXiv Detail & Related papers (2021-01-19T12:23:32Z) - Dual Policy Distillation [58.43610940026261]
Policy distillation, which transfers a teacher policy to a student policy, has achieved great success in challenging tasks of deep reinforcement learning.
In this work, we introduce dual policy distillation(DPD), a student-student framework in which two learners operate on the same environment to explore different perspectives of the environment.
The key challenge in developing this dual learning framework is to identify the beneficial knowledge from the peer learner for contemporary learning-based reinforcement learning algorithms.
arXiv Detail & Related papers (2020-06-07T06:49:47Z)
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.