Undergraduate Programming Courses, Students’ Perception and Success

Mladenović, Saša; Zaharija, Goran; Rosić, Marko

doi:10.1016/j.sbspro.2015.01.1126

Cited by 25 publications

(18 citation statements)

References 9 publications

(8 reference statements)

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“…Computer programming has also turned out to be a core subject in the STEM discipline. In most universities, a Computer Programming course is a threecredit course with a two-hour lecture and two or three-hour lab sessions a week [6,7]. The tutorial session is optional for some universities.…”

Section: Methodsmentioning

confidence: 99%

A Design for Challenge-Based Learning of Programming

Latih

Bakar

Jailani

et al. 2018

Int. J. Adv. Sci. Eng. Inf. Technol.

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Computer Science students are expected to acquire good programming skills. Both students and instructors accept that learning programming for first-year college students is fairly difficult. To assist students to achieve this goal, instructors will have to adopt a suitable design for programming courses. This paper reports on the design of a Computer Programming course based on the integrated course design approach, which was conducted by a research group at Universiti Kebangsaan Malaysia from the Faculty of Information Science and Technology. The course is designed to provide relevant teaching and learning activities, feedback, and assessment that will ultimately support the learning goals of students. The design will provide opportunities for preparation time, meaningful feedback, and a competitive feel to the course. The effectiveness of this approach is then evaluated via an online survey that was administered to first-year undergraduate students. The results obtained from 162 first-year students showed that the students were able to improve the results of their the first-year program with the utilization of PC 2 , as it allowed them to obtain prompt feedback. The use of PC 2 also gives them a competitive atmosphere, which motivates them to perform better. The survey results also indicate that the students used their time to prepare for lab sessions via tutorials and self-learning.

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Section: Methodsmentioning

confidence: 99%

A Design for Challenge-Based Learning of Programming

Latih

Bakar

Jailani

et al. 2018

Int. J. Adv. Sci. Eng. Inf. Technol.

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show abstract

“…Krpan et al's investigation into the correlation between students' success in introductory programming and mathematical courses included an analysis of student expectations and success rates in introductory programming courses at the Faculty of Science, University of Split over three years. They found that early failure to understand basic programming concepts affects students' confidence and leads to increased drop-out rates [11].…”

Section: Related Researchmentioning

confidence: 99%

A Survey of the Prior Programming Experience of Undergraduate Computing and Engineering Students in Ireland

Strong

Higgins²,

Bresnihan

et al. 2017

IFIP Advances in Information and Communication Technology

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It has become apparent that increasing numbers of students arriving into undergraduate computing and engineering degree programmes in Irish 3 rd-level institutions have prior experience of computer programming. As the extent of this prior exposure as well as its nature, origins, and usefulness is not known beyond anecdotal evidence, an annual survey of prior programming experience of freshman undergraduates who study programming as part of their degree has been designed and administered. This paper reports on the first two years of this survey in 2015 and 2016. It found that around one third had some prior experience of programming with nearly half of that group reporting a reasonable level of fluency in one or more languages. The authors expect that the effect of proposed changes to primary and 2 nd-level curricula alongside the increasing popularity of informal programming clubs will be increasingly felt in coming years and therefore plan to continue and extend the survey in order to clarify the effect of such developments. The results should be of interest to 3 rd-level educators in the planning of curriculum and teaching practice.

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“…Student dropout and failure are two major problems faced during the teaching-learning process of computer programming at any education level [1]. These disciplines have high failure rates around the world, sometimes achieving over 50% [1][2][3][4][5]. According to the literature, many factors may contribute to this low approval rate, such as difficulties related to the required abstraction for the proper development of algorithms, difficulties in problem-solving, and also the early stage, in which the programming courses are placed inside the curricula [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Predicting Students Success in Blended Learning—Evaluating Different Interactions Inside Learning Management Systems

et al. 2019

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Algorithms and programming are some of the most challenging topics faced by students during undergraduate programs. Dropout and failure rates in courses involving such topics are usually high, which has raised attention towards the development of strategies to attenuate this situation. Machine learning techniques can help in this direction by providing models able to detect at-risk students earlier. Therefore, lecturers, tutors or staff can pedagogically try to mitigate this problem. To early predict at-risk students in introductory programming courses, we present a comparative study aiming to find the best combination of datasets (set of variables) and classification algorithms. The data collected from Moodle was used to generate 13 distinct datasets based on different aspects of student interactions (cognitive presence, social presence and teaching presence) inside the virtual environment. Results show there are no statistically significant difference among models generated from the different datasets and that the counts of interactions together with derived attributes are sufficient for the task. The performances of the models varied for each semester, with the best of them able to detect students at-risk in the first week of the course with AUC ROC from 0.7 to 0.9. Moreover, the use of SMOTE to balance the datasets did not improve the performance of the models.

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Undergraduate Programming Courses, Students’ Perception and Success

Cited by 25 publications

References 9 publications

A Design for Challenge-Based Learning of Programming

A Design for Challenge-Based Learning of Programming

A Survey of the Prior Programming Experience of Undergraduate Computing and Engineering Students in Ireland

Predicting Students Success in Blended Learning—Evaluating Different Interactions Inside Learning Management Systems

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