research-article

Teaching and learning strategies of programming for university courses

Authors:

José Figueiredo,

Francisco José García-PeñalvoAuthors Info & Claims

TEEM'19: Proceedings of the Seventh International Conference on Technological Ecosystems for Enhancing Multiculturality

Pages 1020 - 1027

https://doi.org/10.1145/3362789.3362926

Published: 16 October 2019 Publication History

Abstract

It is consensual to consider teaching and learning programming difficult. A lot of work, dedication, and motivation are required for teachers and students. Since the first programming languages have emerged, the problem of teaching and learning programming is studied and investigated. The theme is very serious, not only for the important concepts underlying the course but also for the lack of motivation, failure, and abandonment that such frustration may imply in the student. Immediate response and constant monitoring of students' activities and problems are important. With this work, it is our goal to improve student achievement in courses where programming is essential. We want each student to be able to improve and deepen their programming skills, performing a set of exercises appropriate and worked for each student and situation. We intend to build a dynamic learning model of constant evaluation, build the profile of the student. The student profile will be analyzed by our predictive model, which in case of prediction of failure, the student will have more careful attention. Predict the student's failure with anticipation and act with specific activities, giving the student the possibility of training and practicing the activities with difficulties. With this model, we try to improve the skills of each student in programming.

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Cited By

de Oliveira CSobral SFerreira MMoreira F(2024)Interpretable Success Prediction in a Computer Networks Curricular Unit Using Machine LearningProcedia Computer Science10.1016/j.procs.2024.06.212239(598-605)Online publication date: 2024
https://doi.org/10.1016/j.procs.2024.06.212
Juvino de Araujo EAndrade WSouto Oliveira A(2022)Identifying Programming Skills Impacted in Students with Cognitive Disabilities2022 IEEE Frontiers in Education Conference (FIE)10.1109/FIE56618.2022.9962703(1-8)Online publication date: 8-Oct-2022
https://doi.org/10.1109/FIE56618.2022.9962703
Santos JAndrade WBrunet JMelo M(2022)A Systematic Literature Review on Predictive Cognitive Skills in Novice Programming2022 IEEE Frontiers in Education Conference (FIE)10.1109/FIE56618.2022.9962582(1-9)Online publication date: 8-Oct-2022
https://doi.org/10.1109/FIE56618.2022.9962582
Show More Cited By

Index Terms

Teaching and learning strategies of programming for university courses
1. Applied computing
  1. Education

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cover image ACM Other conferences

TEEM'19: Proceedings of the Seventh International Conference on Technological Ecosystems for Enhancing Multiculturality

October 2019

1085 pages

ISBN:9781450371919

DOI:10.1145/3362789

Editors:
Miguel Ángel Conde González
Universidad de León
,
Francisco Jesús Rodríguez Sedano
Universidad de León
,
Camino Fernández Llamas
Universidad de León
,
Francisco José García-Peñalvo
Universidad de Salamanca

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]

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University of Salamanca: University of Salamanca

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 October 2019

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TEEM'19

TEEM'19: Technological Ecosystems for Enhancing Multiculturality

October 16 - 18, 2019

León, Spain

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Overall Acceptance Rate 496 of 705 submissions, 70%

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Cited By

de Oliveira CSobral SFerreira MMoreira F(2024)Interpretable Success Prediction in a Computer Networks Curricular Unit Using Machine LearningProcedia Computer Science10.1016/j.procs.2024.06.212239(598-605)Online publication date: 2024
https://doi.org/10.1016/j.procs.2024.06.212
Juvino de Araujo EAndrade WSouto Oliveira A(2022)Identifying Programming Skills Impacted in Students with Cognitive Disabilities2022 IEEE Frontiers in Education Conference (FIE)10.1109/FIE56618.2022.9962703(1-8)Online publication date: 8-Oct-2022
https://doi.org/10.1109/FIE56618.2022.9962703
Santos JAndrade WBrunet JMelo M(2022)A Systematic Literature Review on Predictive Cognitive Skills in Novice Programming2022 IEEE Frontiers in Education Conference (FIE)10.1109/FIE56618.2022.9962582(1-9)Online publication date: 8-Oct-2022
https://doi.org/10.1109/FIE56618.2022.9962582
Figueiredo JGarcía-Peñalvo F(2020)Intelligent Tutoring Systems approach to Introductory Programming CoursesEighth International Conference on Technological Ecosystems for Enhancing Multiculturality10.1145/3434780.3436614(34-39)Online publication date: 21-Oct-2020
https://dl.acm.org/doi/10.1145/3434780.3436614
García-Peñalvo FGarcía-Holgado ARamírez-Montoya M(2019)Track 16Proceedings of the Seventh International Conference on Technological Ecosystems for Enhancing Multiculturality10.1145/3362789.3362958(920-924)Online publication date: 16-Oct-2019
https://dl.acm.org/doi/10.1145/3362789.3362958

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