Main Article Content
Abstract
University students enrolled in the first year of the Computer Science degree may have problems approaching programming, negatively affecting their study during the course. Tutoring programming projects are very important in helping students with difficulty in learning by providing the right approach to study, improving their knowledge and skills in computing. The aim of this work is to realize a new Java Programming tutoring online course that allows students to have an effective online tool to achieve the learning goals of the course and this will enhance the programming exam pass rate. The course we have designed consists of tools to help students with video tutorials, self-assessment quizzes, code evaluations and exercises to solve using an online Java editor. Because the Moodle platform lacks tools to check the quality of the code syntax, a new software was created. It performs a syntax analysis of the Java code and, as a tutor, automatically provides feedbacks and tips to the students to improve the quality. For each online tool the immediate feedback technique is used to amplify students’ engagement. A Clustering Machine Learning technique is performed to identify different students’ behaviors. A correlation between them and the final performance showed the most influential features of the completed activities. Quantitative analysis highlighted the effectiveness of the tutoring system and the online course designed in this work to enhance the final exam pass rate. At the end, students filled a questionnaire to report their perception and satisfaction about the course.
Keywords
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The author declares that the submitted to Journal of e-Learning and Knowledge Society (Je-LKS) is original and that is has neither been published previously nor is currently being considered for publication elsewhere.
The author agrees that SIe-L (Italian Society of e-Learning) has the right to publish the material sent for inclusion in the journal Je-LKS.
The author agree that articles may be published in digital format (on the Internet or on any digital support and media) and in printed format, including future re-editions, in any language and in any license including proprietary licenses, creative commons license or open access license. SIe-L may also use parts of the work to advertise and promote the publication.
The author declares s/he has all the necessary rights to authorize the editor and SIe-L to publish the work.
The author assures that the publication of the work in no way infringes the rights of third parties, nor violates any penal norms and absolves SIe-L from all damages and costs which may result from publication.
The author declares further s/he has received written permission without limits of time, territory, or language from the rights holders for the free use of all images and parts of works still covered by copyright, without any cost or expenses to SIe-L.
For all the information please check the Ethical Code of Je-LKS, available at http://www.je-lks.org/index.php/ethical-code
References
- Ambrósio A.P., Moreira Costa F., Almeida L, Franco A., Macedo J. (2011), Identifying Cognitive Abilities to Improve CS1 Outcome, 41st ASEE/IEEE Frontiers in Education Conference, Rapid City, SD, USA.
- Amendola D., Miceli C. (2016), Online Physics laboratory for University courses, Journal of E-Learning and Knowledge Society, 12(3), 75-85.
- Amendola D., Miceli C. (2018), Online peer assessment to improve students’ learning outcomes and soft skills, Italian Journal of Educational Technology, 26(3), 71-84.
- Aşkar P., Davenport D. (2009), An investigation of factors related to self-efficacy for Java programming among engineering students, The Turkish Online Journal of Educational Technology (TOJET), 8(1).
- Bishop C.M. (2006), Pattern Recognition and Machine Learning, Springer.
- Bovo A., Sanchez S., Héguy O., Duthen Y. (2013), Clustering Moodle data as a tool for profiling students, Second International Conference on E-Learning and E-Technologies in Education (ICEEE), 121-126.
- Da Re L. (2018), Promoting the academic success: the Formative Tutoring between research and intervention in the experience of the University of Padua, 16(3), 185-199.
- De Santis A., Sannicandro K., Bellini C., Minerva T. (2021), Cluster analysis for tailored tutoring system. Q-TIMES WEBMAGAZINE, 3, 265-277.
- Di Battista D. (2005), The Immediate Feedback Assessment Technique: A Learner-centered Multiple-choice Response Form, The Canadian Journal of Higher Education, 25(4), 111-131
- Epstein M.L., Lazarus A.D., Calvano T.B., Matthews K.A., Hendel R.A., Epstein B.B., Brosvic G.M. (2002), Immediate Feedback Assessment Technique Promotes Learning and Corrects Inaccurate first Responses. Psychol Record 52, 187–201.
- Figueiredo J., García-Peñalvo F.J. (2020), Intelligent Tutoring Systems approach to Introductory Programming Courses, Eighth International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM’20),Association for Computing Machinery, New York, USA, 34–39.
- Figueiredo J., García-Peñalvo F.J. (2018), Building Skills in Introductory Programming, TEEM 2018,
- García-Peñalvo F.J. (2016), What Computational Thinking Is, Journal of Information Technology Research 9(3).
- Gerdes A., Juering J., Heeren B. (2012), An Interactive Functional Programming Tutor, ITiCSE’12, 250-255.
- Guelfi M.R., Masoni M., Shtylla J., Formiconi A.R., (2020) Utilizzo di un MOOC in un corso universitario: studio dell’impatto in termini di apprendimento e gradimento, Reports on E-Learning, Media and Education Meetings, 8(1), 166-171.
- Gusukuma L., Bart A.C., Kafura D., Ernst J. (2018), Misconception-Driven Feedback: Results from an Experimental Study, Proceedings of the 2018 ACM Conference on International Computing Education Research, 160-168.
- Hadjerrouit S. (2008), Towards a Blended Learning Model for Teaching and Learning Computer Programming: A Case Study, Informatics in Education, 7(2), 181-210.
- Hackeling G. (2014), Mastering Machine Learning with Scikit-Learn, Packt Publishing.
- Hattie J., Timperley H. (2007), The Power of Feedback, Review of educational research, 77(1). 81-112.
- Hawi N. (2010), Causal Attributions of Success and Failure Made by Undergraduate Students in an Introductory-Level Computer Programming Course, Computers & Education, 54(4), 1127-1136.
- Marwan S., Williams J. J., Price W.T. (2019), An Evaluation of the Impact of Automated Programming Hints on Performance and Learning, Proceedings of the 2019 ACM Conference on International Computing Education Research, 61–70.
- Nalli G., Amendola D., Perali A., Mostarda L. (2021), Comparative Analysis of Clustering Algorithms and Moodle Plugin for Creation of Student Heterogeneous Groups in Online University Courses, Applied Sciences, 11(13).
- Nalli G, Amendola D, Smith S. (2022), Artificial Intelligence to Improve Learning Outcomes Through Online Collaborative Activities, Proceedings of the 21st European Conference on e-Learning – ECEL 2022, 21(1), 475-479.
- Özmen B., Altun A. (2014), Undergraduate Students’ Experiences in Programming: Difficulties and Obstacles, 5(3), 9-27.
- Richter T., Rudlof S., Adjibadji B., Bernlohr H., Gruninger C., Munz C.D., Stock A., Rohde C., Helmig R. (2012), ViPLab: A Virtual Programming Laboratory for Mathematics and Engineering, Interactive Technology and Smart Education, 9(4), 246-262.
- Robins A., Rountree J., Rountree N. (2010), Learning and Teaching Programming: A Review and Discussion, Computer Science Education, 13(2), 137-172.
- Shute V.J. (2008), Focus on Formative Feedback, Review of Educational Research, 78(1), 153-189.
- Tan J., Guo X., Zheng W., Zhong M. (2014), Case-based teaching using the Laboratory Animal System for learning C/C++ programming, 77, 39-49.
- Tan P., Ting C., Ling S. (2009), Learning Difficulties in Programming Courses: Undergraduates’ Perspective and Perception, International Conference on Computer Technology and Development, 42-46.
- Vellido A., Castro F., Nebot A. (2011), Clustering Educational Data, Handbook of Educational Data Mining, 75-92.
- Wing J.M. (2006), Computational thinking, Communications of the ACM, 49(3), 33-35.