demonstration

Demo of JARET: A.I. Powered Web App for Goal Review and Time Management

Author:

Andrew SchwabeAuthors Info & Claims

L@S '20: Proceedings of the Seventh ACM Conference on Learning @ Scale

Pages 425 - 426

https://doi.org/10.1145/3386527.3405954

Published: 12 August 2020 Publication History

Get Access

Abstract

This demo will present the implementation of an A.I. powered web application designed to assist students with creation of a homework and study schedule. The intent of this project is to use key principles from Self-Regulated Learning and Cognitive Load Theory to translate the large, abstract problem of "creating a study and homework schedule from scratch" into a structured, repeatable set of review tasks. The system then uses a constraint satisfaction AI agent to recommend a weekly schedule of activities that supports the student to achieve the specified goals.

References

[1]

Allan K. Blunt and Timothy A. Pychyl. 2000. Task aversiveness and procrastination: A multi-dimensional approach to task aversiveness across states of personal projects. Retrieved June 15, 2019 from http://scinapse.io/papers/2066142246

Google Scholar

[2]

Alexander Häfner, Verena Oberst and Armin Stock. 2014. Avoiding procrastination through time management: An experimental intervention study. In Educational Studies, 40(3), 352--360 (21 Mar 2014). Retrieved June 15, 2019 from https://doi.org/10.1080/03055698.2014.899487

Crossref

Google Scholar

[3]

Jeroen J. G. van Merriënboer and John Sweller. 2005. Cognitive Load Theory and Complex Learning: Recent Developments and Future Directions. In Educational Psychology Review, 17(2), 147--177 (June 2005). Retrieved June 8, 2019 from https://doi.org/10.1007/s10648-005--3951-0

Crossref

Google Scholar

Cited By

View all

Murad MCollins K(2025)StudyTracker Self-Tracking App and its Relationship to University Student ProcrastinationL'application d’autosuivi StudyTracker et sa relation avec la procrastination des personnes étudiantes universitairesCanadian Journal of Learning and Technology10.21432/cjlt2864450:2(1-23)Online publication date: 11-Jan-2025
https://doi.org/10.21432/cjlt28644
Zhao WHarb HMuntaser MBernacki PRobison JPerri JLemus J(2023)Design and Implementation of a Time Management Self-Help Mobile App for College Students2023 IEEE Integrated STEM Education Conference (ISEC)10.1109/ISEC57711.2023.10402177(81-88)Online publication date: 11-Mar-2023
https://doi.org/10.1109/ISEC57711.2023.10402177
Li JYe ZZhang C(2022)Study on the interaction between big data and artificial intelligenceSystems Research and Behavioral Science10.1002/sres.287839:3(641-648)Online publication date: 12-Jun-2022
https://doi.org/10.1002/sres.2878

Index Terms

Demo of JARET: A.I. Powered Web App for Goal Review and Time Management
1. Computing methodologies
  1. Artificial intelligence
    1. Planning and scheduling
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interactive systems and tools

Recommendations

Promoting self-regulated learning in web-based learning environments

Self-regulated learning with the Internet or hypermedia requires not only cognitive learning strategies, but also specific and general meta-cognitive strategies. The purposes of the Study2000 project, carried out at the TU Dresden, were to develop and ...
GoaLearn App: A Goal-Setting and Monitoring Application to Support Students’ Self-regulated Learning
Technology Enhanced Learning for Inclusive and Equitable Quality Education
Abstract
Self-regulated learning (SRL) stands as a crucial competency for learners in the 21st century. Research showed that equipped with strategies to regulate their learning and development, students demonstrated higher academic achievements and ...
Self-regulated learning strategies predict learner behavior and goal attainment in Massive Open Online Courses

Individuals with strong self-regulated learning (SRL) skills, characterized by the ability to plan, manage and control their learning process, can learn faster and outperform those with weaker SRL skills. SRL is critical in learning environments that ...

Comments

Information & Contributors

Information

Published In

L@S '20: Proceedings of the Seventh ACM Conference on Learning @ Scale

August 2020

442 pages

ISBN:9781450379519

DOI:10.1145/3386527

General Chair:
David Joyner
Georgia Tech
,
Program Chairs:
Rene Kizilcec
Cornell University
,
Susan Singer
Rollins College

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 August 2020

Check for updates

Author Tags

Qualifiers

Demonstration

Conference

L@S '20

L@S '20: Seventh (2020) ACM Conference on Learning @ Scale

August 12 - 14, 2020

Virtual Event, USA

Acceptance Rates

Overall Acceptance Rate 117 of 440 submissions, 27%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
179
Total Downloads

Downloads (Last 12 months)21
Downloads (Last 6 weeks)1

Reflects downloads up to 06 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Murad MCollins K(2025)StudyTracker Self-Tracking App and its Relationship to University Student ProcrastinationL'application d’autosuivi StudyTracker et sa relation avec la procrastination des personnes étudiantes universitairesCanadian Journal of Learning and Technology10.21432/cjlt2864450:2(1-23)Online publication date: 11-Jan-2025
https://doi.org/10.21432/cjlt28644
Zhao WHarb HMuntaser MBernacki PRobison JPerri JLemus J(2023)Design and Implementation of a Time Management Self-Help Mobile App for College Students2023 IEEE Integrated STEM Education Conference (ISEC)10.1109/ISEC57711.2023.10402177(81-88)Online publication date: 11-Mar-2023
https://doi.org/10.1109/ISEC57711.2023.10402177
Li JYe ZZhang C(2022)Study on the interaction between big data and artificial intelligenceSystems Research and Behavioral Science10.1002/sres.287839:3(641-648)Online publication date: 12-Jun-2022
https://doi.org/10.1002/sres.2878

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Promoting self-regulated learning in web-based learning environments

GoaLearn App: A Goal-Setting and Monitoring Application to Support Students’ Self-regulated Learning

Self-regulated learning strategies predict learner behavior and goal attainment in Massive Open Online Courses

Comments

Information

Published In

Publisher

Publication History

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations