short-paper

Body of Knowledge Explorer: Long-Term Student Guidance Across the Computer-Science Domain

Author:

Vangel V. AjanovskiAuthors Info & Claims

Koli Calling '19: Proceedings of the 19th Koli Calling International Conference on Computing Education Research

Article No.: 5, Pages 1 - 5

https://doi.org/10.1145/3364510.3364531

Published: 21 November 2019 Publication History

Abstract

A visual domain exploration tool is introduced that offers several viewpoints as parts of a personalized student advice and guidance solution: explanation of the personal progress over a reference body of knowledge areas, within the field of study; mapping of the actual courses in relation to the field of study where the taught topics belong; guidance in choosing courses aligned towards personal interests and abilities in respective areas. The student can visualize areas that are successfully covered by past courses, areas that are a probable point of risk, mandating greater focus and determination, and out-of-topic areas where the student is predicted to perform with greater success. The student can view the past trends of personal progress, and experiment with future tracks.

References

[1]

Sunita B. Aher and LMRJ Lobo. 2012. Course Recommender System in E-Learning. International Journal of Computer Science and Communication 3, 1 (2012), 159--164. https://pdfs.semanticscholar.org/04b7/3e2335c2caffe26c78c8aeee33a76e900504.pdf

[2]

Vangel V. Ajanovski. 2017. Curriculum Mapping As a Tool for Improving Students Satisfaction with the Choice of Courses. In Proceedings of the 2017 ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE '17). ACM, Bologna, Italy, 76--77. https://doi.org/10.1145/3059009.3072978

Digital Library

[3]

Vangel V. Ajanovski. 2017. Guided exploration of the domain space of study programs: Recommenders in improving student awareness on the choices made during enrollment. In Proceedings of the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (INTRS'17), Vol. 1884. CEUR-WS, Como, Italy, 43--47.

[4]

Vangel V. Ajanovski. 2018. Personalized Long-term Student Guidance Towards Graduation. In Proceedings of EdMedia: World Conference on Educational Media and Technology (EdMedia). Association for the Advancement of Computing in Education (AACE), Amsterdam, Netherlands, 1458--1463. https://www.learntechlib.org/primary/p/184364/

[5]

Vangel V. Ajanovski. 2019. Student Information Systems of the Next Generation. https://develop.finki.ukim.mk/projects/sisng

[6]

Tapio Auvinen, Juha Paavola, and Juha Hartikainen. 2014. STOPS: A Graph-based Study Planning and Curriculum Development Tool. In Proceedings of the 14th Koli Calling International Conference on Computing Education Research (Koli Calling '14). ACM, New York, NY, USA, 25--34. https://doi.org/10.1145/2674683.2674689

Digital Library

[7]

Rosta Farzan and Peter Brusilovsky. 2011. Encouraging user participation in a course recommender system: An impact on user behavior. Computers in Human Behavior 27, 1 (Jan. 2011), 276--284. https://doi.org/10.1016/j.chb.2010.08.005

Digital Library

[8]

Bettina Grün and Kurt Hornik. 2011. topicmodels: An R Package for Fitting Topic Models. Journal of Statistical Software 40, 13 (2011). https://doi.org/10.18637/jss.v040.i13

[9]

Nina Hagemann, Michael P. O'Mahony, and Barry Smyth. 2018. Module Advisor: Guiding Students with Recommendations. In Intelligent Tutoring Systems (Lecture Notes in Computer Science), Roger Nkambou, Roger Azevedo, and Julita Vassileva (Eds.). Springer International Publishing, 319--325.

Digital Library

[10]

Nina Hagemann, Michael P. O'Mahony, and Barry Smyth. 2019. Visualising Module Dependencies in Academic Recommendations. In Proceedings of the 24th International Conference on Intelligent User Interfaces: Companion (IUI '19). ACM, New York, NY, USA, 77--78. https://doi.org/10.1145/3308557.3308701 event-place: Marina del Ray, California.

Digital Library

[11]

Arto Hellas, Petri Ihantola, Andrew Petersen, Vangel V. Ajanovski, Mirela Gutica, Timo Hynninen, Antti Knutas, Juho Leinonen, Chris Messom, and Soohyun Nam Liao. 2018. Predicting Academic Performance: A Systematic Literature Review. In Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE 2018 Companion). ACM, New York, NY, USA, 175--199. https://doi.org/10.1145/3293881.3295783

Digital Library

[12]

IEEE Computer Joint Task Force on Computing Curricula, Association for Computing Machinery (ACM) and IEEE Computer Society. 2013. Computer Science Curricula 2013: Curriculum Guidelines for Undergraduate Degree Programs in Computer Science. ACM, New York, NY, USA. https://dx.doi.org/10.1145/2534860

[13]

Kornraphop Kawintiranon, Peerapon Vateekul, Atiwong Suchato, and Proadpran Punyabukkana. 2016. Understanding knowledge areas in curriculum through text mining from course materials. IEEE, 161--168. https://doi.org/10.1109/TALE.2016.7851788

[14]

Georgia Koutrika, Benjamin Bercovitz, Filip Kaliszan, Henry Liou, and Hector Garcia-Molina. 2009. CourseRank: A Closed-Community Social System through the Magnifying Glass. In Proceedings of the Third International ICWSM Conference (2009). AAAI, San Jose Mc Enery Convention Center, USA, 98--105. https://www.aaai.org/ocs/index.php/ICWSM/09/paper/view/228

[15]

Guido Kraemer, Markus Reichstein, and Miguel Mahecha D. 2018. dimRed and coRanking---Unifying Dimensionality Reduction in R. The R Journal 10, 1 (2018), 342--358. https://doi.org/10.32614/RJ-2018-039

[16]

Alisa Krstova, Bozhidar Stevanoski, Marija Mihova, and Vangel V. Ajanovski. 2018. Initialization of Matrix Factorization Methods for University Course Recommendations Using SimRank Similarities. In ICT Innovations 2018. Engineering and Life Sciences (Communications in Computer and Information Science), Slobodan Kalajdziski and Nevena Ackovska (Eds.). Springer International Publishing, Ohrid, Macedonia, 172--184.

[17]

Louise Ann Lyon and Jill Denner. 2016. Student perspectives of community college pathways to computer science bachelor's degrees / VOCEDplus, the international tertiary education and research database. Technical Report. https://www.voced.edu.au/content/ngv:78350

[18]

Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing Data using t-SNE. Journal of Machine Learning Research 9, Nov (2008), 2579--2605. http://www.jmlr.org/papers/v9/vandermaaten08a.html

[19]

John G. Meinke and Charles R. Bauer. 1976. Topological Sorting As a Tool in Curriculum Planning. ACM SIGCSE Bulletin 8, 3 (July 1976), 61--66. https://doi.org/10.1145/952991.804757

Digital Library

[20]

Andriy Mnih and Ruslan R. Salakhutdinov. 2007. Probabilistic matrix factorization. In NIPS'07 Proceedings of the 20th International Conference on Neural Information Processing Systems. Curran Associates Inc., Vancouver, British Columbia, Canada, 1257--1264. http://papers.nips.cc/paper/3208-probabilistic-matrix-factorization.pdf

[21]

Sara Morsy and George Karypis. 2017. Cumulative Knowledge-based Regression Models for Next-term Grade Prediction. In Proceedings of the 2017 SIAM International Conference on Data Mining. SIAM, Houston, Texas, USA, 552--560. http://epubs.siam.org/doi/abs/10.1137/1.9781611974973.62

[22]

Sara Morsy and George Karypis. 2019. A Study on Curriculum Planning and Its Relationship with Graduation GPA and Time To Degree. In Proceedings of the 9th International Conference on Learning Analytics & Knowledge - LAK19. ACM Press, Tempe, AZ, USA, 26--35. https://doi.org/10.1145/3303772.3303783

Digital Library

[23]

Michael P. O'Mahony and Barry Smyth. 2007. A Recommender System for On-line Course Enrolment: An Initial Study. In Proceedings of the 2007 ACM Conference on Recommender Systems (RecSys '07). ACM, New York, NY, USA, 133--136. https://doi.org/10.1145/1297231.1297254

Digital Library

[24]

Ruslan Salakhutdinov and Andriy Mnih. 2008. Bayesian Probabilistic Matrix Factorization Using Markov Chain Monte Carlo. In Proceedings of the 25th International Conference on Machine Learning (ICML '08). ACM, New York, NY, USA, 880--887. https://doi.org/10.1145/1390156.1390267

Digital Library

[25]

Judith Scott-Clayton. 2011. The Shapeless River: Does a Lack of Structure Inhibit Students' Progress at Community Colleges? CCRC Working Paper No. 25. Assessment of Evidence Series. Community College Research Center. https://eric.ed.gov/?id=ED515131

[26]

Takayuki Sekiya, Yoshitatsu Matsuda, and Kazunori Yamaguchi. 2010. Analysis of Computer Science Related Curriculum on LDA and Isomap. In Proceedings of the Fifteenth Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE '10). ACM, New York, NY, USA, 48--52. https://doi.org/10.1145/1822090.1822106

Digital Library

[27]

Takayuki Sekiya, Yoshitatsu Matsuda, and Kazunori Yamaguchi. 2015. Curriculum Analysis of CS Departments Based on CS2013 by Simplified, Supervised LDA. In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge (LAK '15). ACM, New York, NY, USA, 330--339. https://doi.org/10.1145/2723576.2723594

[28]

Mark Sheehan and Young Park. 2012. pGPA: A Personalized Grade Prediction Tool to Aid Student Success. In Proceedings of the Sixth ACM Conference on Recommender Systems (RecSys '12). ACM, New York, NY, USA, 309--310. https://doi.org/10.1145/2365952.2366027

Digital Library

[29]

Yunhong Zhou, Dennis Wilkinson, Robert Schreiber, and Rong Pan. 2008. Large-Scale Parallel Collaborative Filtering for the Netflix Prize. In Algorithmic Aspects in Information and Management (Lecture Notes in Computer Science). Springer, Berlin, Heidelberg, 337--348. https://doi.org/10.1007/978-3-540-68880-8_32

[30]

Ron Zucker. 2009. ViCurriAS: A Curriculum Visualization Tool for Faculty, Advisors, and Students. J. Comput. Sci. Coll. 25, 2 (Dec. 2009), 138--145. http://dl.acm.org/citation.cfm?id=1629036.1629059

Cited By

Ott CMills SStanger NJohn SZwanenburg S(2021)Is this Degree for Me?Exploring computing students’ study decisionsProceedings of the 23rd Australasian Computing Education Conference10.1145/3441636.3442310(96-105)Online publication date: 2-Feb-2021
https://dl.acm.org/doi/10.1145/3441636.3442310
Mäses SMaennel OSütterlin S(2021)Using Competency Mapping for Skills Assessment in an Introductory Cybersecurity CourseEducating Engineers for Future Industrial Revolutions10.1007/978-3-030-68201-9_56(572-583)Online publication date: 14-Mar-2021
https://doi.org/10.1007/978-3-030-68201-9_56
Ajanovski V(2020)What to study next?Companion Proceedings of the 25th International Conference on Intelligent User Interfaces10.1145/3379336.3381454(57-58)Online publication date: 17-Mar-2020
https://dl.acm.org/doi/10.1145/3379336.3381454
Show More Cited By

Index Terms

Body of Knowledge Explorer: Long-Term Student Guidance Across the Computer-Science Domain
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. Information systems applications
    1. Collaborative and social computing systems and tools
      1. Open source software
2. Social and professional topics
  1. Professional topics
    1. Computing education
      1. Model curricula

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Koli Calling '19: Proceedings of the 19th Koli Calling International Conference on Computing Education Research

November 2019

247 pages

ISBN:9781450377157

DOI:10.1145/3364510

Conference Chairs:
Petri Ihantola
University of Helsinki, Finland
,
Nick Falkner
University of Adelaide, Australia

Copyright © 2019 ACM.

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]

In-Cooperation

University of Helsinki
Univ. Eastern Finland: University of Eastern Finland
The University of Adelaide, Australia
SIGCSE: ACM Special Interest Group on Computer Science Education

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 November 2019

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Koli Calling '19

Koli Calling '19: 19th Koli Calling International Conference on Computing Education Research

November 21 - 24, 2019

Koli, Finland

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Cited By

Ott CMills SStanger NJohn SZwanenburg S(2021)Is this Degree for Me?Exploring computing students’ study decisionsProceedings of the 23rd Australasian Computing Education Conference10.1145/3441636.3442310(96-105)Online publication date: 2-Feb-2021
https://dl.acm.org/doi/10.1145/3441636.3442310
Mäses SMaennel OSütterlin S(2021)Using Competency Mapping for Skills Assessment in an Introductory Cybersecurity CourseEducating Engineers for Future Industrial Revolutions10.1007/978-3-030-68201-9_56(572-583)Online publication date: 14-Mar-2021
https://doi.org/10.1007/978-3-030-68201-9_56
Ajanovski V(2020)What to study next?Companion Proceedings of the 25th International Conference on Intelligent User Interfaces10.1145/3379336.3381454(57-58)Online publication date: 17-Mar-2020
https://dl.acm.org/doi/10.1145/3379336.3381454
Ajanovski VGiannakos MSindre GLuxton-Reilly ADivitini M(2020)Tools for Analysis of Curricula Evolution Across Computer Science Curriculum GuidelinesProceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education10.1145/3341525.3393995(539-540)Online publication date: 15-Jun-2020
https://dl.acm.org/doi/10.1145/3341525.3393995

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