skip to main content
10.1145/2948674.2948676acmotherconferencesArticle/Chapter ViewAbstractPublication PagesmodConference Proceedingsconference-collections
research-article

CourseNavigator: interactive learning path exploration

Published: 26 June 2016 Publication History

Abstract

Course selection decision making is an extremely tedious task that needs to consider course prerequisites, degree requirements, class schedules, as well as the student's preferences and constraints. As a result, students often make short term decisions based on imprecise information without deep understanding of the longer-term impact on their education goal and in most cases without good understanding of the alternative options. In this paper, we introduce CourseNavigator, a new course exploration service that attempts to address the course exploration challenge. Our service identifies all possible course selection options for a given academic period, referred to as learning paths, that can meet the student's customized goals and constraints. CourseNavigator offers a suite of learning path generation algorithms designed to meet a range of course exploration end-goals such as learning paths for a given period and desired degree as well as the highest ranked paths based on user-defined ranking functions. Our techniques rely on a graph-search algorithm for enumerating candidate learning paths and employ a number of strategies (i.e., early detection of dead-end paths, limiting the exploration to strategic course selections) for improving the exploration efficiency.

References

[1]
MSU Degree Navigator, https://degnav.msu.edu/.
[2]
Rutgers Degree Navigator, https://nbdn.rutgers.edu/.
[3]
Parameswaran et al. Recommendation systems with complex constraints: A course recommendation perspective. ACM Trans. Inf. Syst., 29(4):20:1--20:33, Dec. 2011.
[4]
Chung et al. Ontology design for creating adaptive learning path in e-learning environment. In IMECS, 2012.
[5]
Pirrone et al. Learning path generation by domain ontology transformation. In AI*IA, 2005.
[6]
M. K. Stern and B. P. Woolf. Curriculum sequencing in a web-based tutor. In Intelligent Tutoring Systems, 1998.
[7]
J. Wei, G. Koutrika, and S. Wu. Learn2learn: A visual educational system for study planning. In EDBT, 2014.

Cited By

View all
  • (2024)A model to create a personalized online course based on the student’s learning stylesEducation and Information Technologies10.1007/s10639-023-12287-229:1(571-593)Online publication date: 1-Jan-2024
  • (2023)Fully Individualized Curriculum with Decaying Knowledge, a New Hard Problem: Investigation and RecommendationsInternational Journal of Artificial Intelligence in Education10.1007/s40593-023-00376-9Online publication date: 20-Nov-2023
  • (2021)A Knowledge Graph Embedding Based Approach for Learning Path Recommendation for Career GoalsComputational Collective Intelligence10.1007/978-3-030-88081-1_6(66-78)Online publication date: 30-Sep-2021
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
ExploreDB '16: Proceedings of the Third International Workshop on Exploratory Search in Databases and the Web
June 2016
38 pages
ISBN:9781450343121
DOI:10.1145/2948674
Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

  • LogicBlox: LogicBlox Inc.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 June 2016

Permissions

Request permissions for this article.

Check for updates

Qualifiers

  • Research-article

Conference

SIGMOD/PODS'16
Sponsor:
  • LogicBlox
SIGMOD/PODS'16: International Conference on Management of Data
June 26 - July 1, 2016
California, San Francisco

Acceptance Rates

ExploreDB '16 Paper Acceptance Rate 5 of 11 submissions, 45%;
Overall Acceptance Rate 11 of 21 submissions, 52%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)11
  • Downloads (Last 6 weeks)1
Reflects downloads up to 15 Sep 2024

Other Metrics

Citations

Cited By

View all
  • (2024)A model to create a personalized online course based on the student’s learning stylesEducation and Information Technologies10.1007/s10639-023-12287-229:1(571-593)Online publication date: 1-Jan-2024
  • (2023)Fully Individualized Curriculum with Decaying Knowledge, a New Hard Problem: Investigation and RecommendationsInternational Journal of Artificial Intelligence in Education10.1007/s40593-023-00376-9Online publication date: 20-Nov-2023
  • (2021)A Knowledge Graph Embedding Based Approach for Learning Path Recommendation for Career GoalsComputational Collective Intelligence10.1007/978-3-030-88081-1_6(66-78)Online publication date: 30-Sep-2021
  • (2020)Package recommender systems: A systematic reviewIntelligent Decision Technologies10.3233/IDT-19014013:4(435-452)Online publication date: 10-Feb-2020
  • (2018)Estimating time and score uncertainty in generating successful learning paths under time constraintsExpert Systems10.1111/exsy.1235136:2Online publication date: 24-Oct-2018
  • (2017)RUTICOAdjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization10.1145/3099023.3099035(153-158)Online publication date: 9-Jul-2017

View Options

Get Access

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media