article

Student progress monitoring tool using treeview

Authors:

Jungsoon Yoo,

Sung Yoo,

Chris Lance,

Judy HankinsAuthors Info & Claims

ACM SIGCSE Bulletin, Volume 38, Issue 1

Pages 373 - 377

https://doi.org/10.1145/1124706.1121456

Published: 03 March 2006 Publication History

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Abstract

In this paper, we present an extensible visualization tool that is being used in a web-based closed laboratory system. The goal of this project is to provide a tool for both students and teachers that can help trace deficiencies in a student's understanding back to individual concepts. This visualization tool has been developed by using the tree abstract data type (ADT) which is built from the concepts to be covered in a lab, lecture, or course. Once the tree ADT is built, each node can be associated with different entities such as student performance, class performance, or lab development. Using this tool, a teacher can help students by discovering concepts that need additional lecture coverage, and students may discover concepts for which they need to spend additional time working on reinforcement exercises.

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Cited By

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Kalpana Chauhan SSingh MBagoria R(2023)Novel Taxonomy for E-learning Recommender System Using Opinion MiningAdvanced Computing10.1007/978-3-031-35644-5_30(374-385)Online publication date: 14-Jul-2023
https://doi.org/10.1007/978-3-031-35644-5_30
Dermy ORoussanaly ABoyer A(2022)Using Behavioral Primitives to Model Students’ Digital BehaviorProcedia Computer Science10.1016/j.procs.2022.09.302207(2444-2453)Online publication date: 2022
https://doi.org/10.1016/j.procs.2022.09.302
Jeghal AOughdir LTairi HRadouane A(2018)Approach for using Learner Satisfaction to Evaluate the Learning Adaptation PolicyInternational Journal of Distance Education Technologies10.4018/IJDET.201610010114:4(1-12)Online publication date: 19-Dec-2018
https://dl.acm.org/doi/10.4018/IJDET.2016100101
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Index Terms

Student progress monitoring tool using treeview
1. Applied computing
  1. Education

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Information

Published In

ACM SIGCSE Bulletin Volume 38, Issue 1

March 2006

553 pages

ISSN:0097-8418

DOI:10.1145/1124706

Issue’s Table of Contents

SIGCSE '06: Proceedings of the 37th SIGCSE technical symposium on Computer science education
March 2006
612 pages
ISBN:1595932593
DOI:10.1145/1121341
Program Chairs:
Doug Baldwin
SUNY Geneseo
,
Paul Tymann
Rochester Institute of Technology
,
Susan Haller
SUNY Potsdam
,
Ingrid Russell
University of Hartford

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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 March 2006

Published in SIGCSE Volume 38, Issue 1

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Cited By

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Kalpana Chauhan SSingh MBagoria R(2023)Novel Taxonomy for E-learning Recommender System Using Opinion MiningAdvanced Computing10.1007/978-3-031-35644-5_30(374-385)Online publication date: 14-Jul-2023
https://doi.org/10.1007/978-3-031-35644-5_30
Dermy ORoussanaly ABoyer A(2022)Using Behavioral Primitives to Model Students’ Digital BehaviorProcedia Computer Science10.1016/j.procs.2022.09.302207(2444-2453)Online publication date: 2022
https://doi.org/10.1016/j.procs.2022.09.302
Jeghal AOughdir LTairi HRadouane A(2018)Approach for using Learner Satisfaction to Evaluate the Learning Adaptation PolicyInternational Journal of Distance Education Technologies10.4018/IJDET.201610010114:4(1-12)Online publication date: 19-Dec-2018
https://dl.acm.org/doi/10.4018/IJDET.2016100101
Kalpana Bagoria R(2024)Enhancing E-Learning: A Comprehensive Guide to Performance Metrics and Evaluation2024 Sixth International Conference on Computational Intelligence and Communication Technologies (CCICT)10.1109/CCICT62777.2024.00017(41-46)Online publication date: 19-Apr-2024
https://doi.org/10.1109/CCICT62777.2024.00017
Ang KGe FSeng K(2020)Big Educational Data & Analytics: Survey, Architecture and ChallengesIEEE Access10.1109/ACCESS.2020.29945618(116392-116414)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2994561
Martínez‐Carrascal JMárquez Cebrián DSancho‐Vinuesa TValderrama E(2020)Impact of early activity on flipped classroom performance prediction: A case study for a first‐year Engineering courseComputer Applications in Engineering Education10.1002/cae.2222928:3(590-605)Online publication date: 26-Mar-2020
https://doi.org/10.1002/cae.22229
Aldowah HAl-Samarraie HFauzy W(2019)Educational Data Mining and Learning Analytics for 21stcentury higher education: A Review and SynthesisTelematics and Informatics10.1016/j.tele.2019.01.007Online publication date: Jan-2019
https://doi.org/10.1016/j.tele.2019.01.007
Gottipati SShankararaman V(2018)Competency analytics toolEducation and Information Technologies10.1007/s10639-017-9584-323:1(41-60)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1007/s10639-017-9584-3
Venkatachalapathy KVijayalakshmi VOhmprakash V(2017)Educational Data Mining Tools: A Survey from 2001 to 20162017 Second International Conference on Recent Trends and Challenges in Computational Models (ICRTCCM)10.1109/ICRTCCM.2017.53(67-72)Online publication date: Feb-2017
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Gottipati SShankararaman VGoh M(2017)Mining Capstone Project Wikis for Knowledge Discovery2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC)10.1109/COMPSAC.2017.169(371-380)Online publication date: Jul-2017
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