Show me the numbers! -- Student-facing Interventions in Adaptive
Learning Environments for German Spelling
- URL: http://arxiv.org/abs/2306.07853v1
- Date: Tue, 13 Jun 2023 15:33:09 GMT
- Title: Show me the numbers! -- Student-facing Interventions in Adaptive
Learning Environments for German Spelling
- Authors: Nathalie Rzepka, Katharina Simbeck, Hans-Georg Mueller, Marlene
Bueltemann, Niels Pinkwart
- Abstract summary: Student-facing adaptive learning environments are effective in improving a persons error rate.
We evaluate the different interventions with regard to the error rate, the number of early dropouts, and the users competency.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Since adaptive learning comes in many shapes and sizes, it is crucial to find
out which adaptations can be meaningful for which areas of learning. Our work
presents the result of an experiment conducted on an online platform for the
acquisition of German spelling skills. We compared the traditional online
learning platform to three different adaptive versions of the platform that
implement machine learning-based student-facing interventions that show the
personalized solution probability. We evaluate the different interventions with
regard to the error rate, the number of early dropouts, and the users
competency. Our results show that the number of mistakes decreased in
comparison to the control group. Additionally, an increasing number of dropouts
was found. We did not find any significant effects on the users competency. We
conclude that student-facing adaptive learning environments are effective in
improving a persons error rate and should be chosen wisely to have a motivating
impact.
Related papers
- Toward In-Context Teaching: Adapting Examples to Students' Misconceptions [54.82965010592045]
We introduce a suite of models and evaluation methods we call AdapT.
AToM is a new probabilistic model for adaptive teaching that jointly infers students' past beliefs and optimize for the correctness of future beliefs.
Our results highlight both the difficulty of the adaptive teaching task and the potential of learned adaptive models for solving it.
arXiv Detail & Related papers (2024-05-07T17:05:27Z) - Corrective Machine Unlearning [22.342035149807923]
We formalize Corrective Machine Unlearning as the problem of mitigating the impact of data affected by unknown manipulations on a trained model.
We find most existing unlearning methods, including retraining-from-scratch without the deletion set, require most of the manipulated data to be identified for effective corrective unlearning.
One approach, Selective Synaptic Dampening, achieves limited success, unlearning adverse effects with just a small portion of the manipulated samples in our setting.
arXiv Detail & Related papers (2024-02-21T18:54:37Z) - Getting too personal(ized): The importance of feature choice in online
adaptive algorithms [6.716421415117937]
We consider whether and when attempting to discover how to personalize has a cost, such as if the adaptation to personal information can delay the adoption of policies that benefit all students.
We explore these issues in the context of using multi-armed bandit (MAB) algorithms to learn a policy for what version of an educational technology to present to each student.
We demonstrate that the inclusion of student characteristics for personalization can be beneficial when those characteristics are needed to learn the optimal action.
arXiv Detail & Related papers (2023-09-06T09:34:54Z) - Towards Scalable Adaptive Learning with Graph Neural Networks and
Reinforcement Learning [0.0]
We introduce a flexible and scalable approach towards the problem of learning path personalization.
Our model is a sequential recommender system based on a graph neural network.
Our results demonstrate that it can learn to make good recommendations in the small-data regime.
arXiv Detail & Related papers (2023-05-10T18:16:04Z) - ASPEST: Bridging the Gap Between Active Learning and Selective
Prediction [56.001808843574395]
Selective prediction aims to learn a reliable model that abstains from making predictions when uncertain.
Active learning aims to lower the overall labeling effort, and hence human dependence, by querying the most informative examples.
In this work, we introduce a new learning paradigm, active selective prediction, which aims to query more informative samples from the shifted target domain.
arXiv Detail & Related papers (2023-04-07T23:51:07Z) - Adoption of Artificial Intelligence in Schools: Unveiling Factors
Influencing Teachers Engagement [5.546987319988426]
AI tools adopted in schools may not always be considered and studied products of the research community.
We developed a reliable instrument to measure more holistic factors influencing teachers adoption of adaptive learning platforms in schools.
Not generating any additional workload, in-creasing teacher ownership and trust, generating support mechanisms for help, and assuring that ethical issues are minimised are also essential for the adoption of AI in schools.
arXiv Detail & Related papers (2023-04-03T11:47:08Z) - When Do Curricula Work in Federated Learning? [56.88941905240137]
We find that curriculum learning largely alleviates non-IIDness.
The more disparate the data distributions across clients the more they benefit from learning.
We propose a novel client selection technique that benefits from the real-world disparity in the clients.
arXiv Detail & Related papers (2022-12-24T11:02:35Z) - 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) - Efficient Estimation of Influence of a Training Instance [56.29080605123304]
We propose an efficient method for estimating the influence of a training instance on a neural network model.
Our method is inspired by dropout, which zero-masks a sub-network and prevents the sub-network from learning each training instance.
We demonstrate that the proposed method can capture training influences, enhance the interpretability of error predictions, and cleanse the training dataset for improving generalization.
arXiv Detail & Related papers (2020-12-08T04:31:38Z) - A framework for predicting, interpreting, and improving Learning
Outcomes [0.0]
We develop an Embibe Score Quotient model (ESQ) to predict test scores based on observed academic, behavioral and test-taking features of a student.
ESQ can be used to predict the future scoring potential of a student as well as offer personalized learning nudges.
arXiv Detail & Related papers (2020-10-06T11:22:27Z) - Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic
Reinforcement Learning [109.77163932886413]
We show how to adapt vision-based robotic manipulation policies to new variations by fine-tuning via off-policy reinforcement learning.
This adaptation uses less than 0.2% of the data necessary to learn the task from scratch.
We find that our approach of adapting pre-trained policies leads to substantial performance gains over the course of fine-tuning.
arXiv Detail & Related papers (2020-04-21T17:57:04Z)
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.