Learning Outcome Oriented Programmatic Assessment
- URL: http://arxiv.org/abs/2101.10133v1
- Date: Tue, 19 Jan 2021 22:36:53 GMT
- Title: Learning Outcome Oriented Programmatic Assessment
- Authors: Pum Walters, Michael Nieweg, James Watson
- Abstract summary: This paper describes considerations behind the organisation of a third semester BSc education.
The project aims to facilitate a feedback-oriented environment using assessment for learning and for incremental measure of learner progress.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper describes considerations behind the organisation of a third
semester BSc education. The project aims to facilitate a feedback-oriented
environment using assessment for learning and for incremental measure of
learner progress [Vleuten et al, 2012, "A model for programmatic assessment fit
for purpose"]. Learning outcomes encourage higher order cognitive skills,
following [Biggs & Tang, 2011,"Teaching for quality learning at university:
what the student does"]. Embracing [Dochy et al. 2018, "Creating Impact Through
Future Learning: The High Impact Learning that Lasts (HILL) Model"], several
mechanisms encourage focus and motivation.
Related papers
- ReLearn: Unlearning via Learning for Large Language Models [64.2802606302194]
We propose ReLearn, a data augmentation and fine-tuning pipeline for effective unlearning.
This framework introduces Knowledge Forgetting Rate (KFR) and Knowledge Retention Rate (KRR) to measure knowledge-level preservation.
Our experiments show that ReLearn successfully achieves targeted forgetting while preserving high-quality output.
arXiv Detail & Related papers (2025-02-16T16:31:00Z) - The Influence and Relationship between Computational Thinking, Learning Motivation, Attitude, and Achievement of Code.org in K-12 Programming Education [0.0]
This study examined the impact of Code.org's block-based coding curriculum on primary school students' computational thinking, motivation, attitudes, and academic performance.
arXiv Detail & Related papers (2024-12-05T00:12:26Z) - The Perceived Learning Behaviors and Assessment Techniques of First-Year Students in Computer Science: An Empirical Study [0.0]
Students believe that in-person instruction is the most effective way to learn.
For evaluation methods, there is a preference for practical and written examinations.
arXiv Detail & Related papers (2024-05-10T08:45:32Z) - A General Model for Detecting Learner Engagement: Implementation and Evaluation [0.0]
This paper proposes a general, lightweight model for selecting and processing features to detect learners' engagement levels.
We analyzed the videos from the publicly available DAiSEE dataset to capture the dynamic essence of learner engagement.
The suggested model achieves an accuracy of 68.57% in a specific implementation and outperforms the studied state-of-the-art models detecting learners' engagement levels.
arXiv Detail & Related papers (2024-05-07T12:11:15Z) - Enhancing Generative Class Incremental Learning Performance with Model Forgetting Approach [50.36650300087987]
This study presents a novel approach to Generative Class Incremental Learning (GCIL) by introducing the forgetting mechanism.
We have found that integrating the forgetting mechanisms significantly enhances the models' performance in acquiring new knowledge.
arXiv Detail & Related papers (2024-03-27T05:10:38Z) - 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) - Towards Goal-oriented Intelligent Tutoring Systems in Online Education [69.06930979754627]
We propose a new task, named Goal-oriented Intelligent Tutoring Systems (GITS)
GITS aims to enable the student's mastery of a designated concept by strategically planning a customized sequence of exercises and assessment.
We propose a novel graph-based reinforcement learning framework, named Planning-Assessment-Interaction (PAI)
arXiv Detail & Related papers (2023-12-03T12:37:16Z) - A Hierarchy-based Analysis Approach for Blended Learning: A Case Study
with Chinese Students [12.533646830917213]
This paper proposes a hierarchy-based evaluation approach for blended learning evaluation.
The results show that cognitive engagement and emotional engagement play a more important role in blended learning evaluation.
arXiv Detail & Related papers (2023-09-19T00:09:00Z) - Procedure Planning in Instructional Videosvia Contextual Modeling and
Model-based Policy Learning [114.1830997893756]
This work focuses on learning a model to plan goal-directed actions in real-life videos.
We propose novel algorithms to model human behaviors through Bayesian Inference and model-based Imitation Learning.
arXiv Detail & Related papers (2021-10-05T01:06:53Z) - 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)
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.