Failure rates in introductory programming

Bennedsen, Jens; Caspersen, Michael E.

doi:10.1145/3324888

Cited by 177 publications

(85 citation statements)

References 10 publications

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“…Promoting effective learning and student success remains a key issue in higher education, particularly in the area of computer science, with high failure rates reported for introductory programming in particular [5,45]. As such, considerable work has been undertaken to understand the factors that have influence on these outcomes.…”

Section: Discussionmentioning

confidence: 99%

Resilience and Effective Learning in First-Year Undergraduate Computer Science

Prickett

Walters

Yang

et al. 2020

Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education

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Many factors have been shown to be important for supporting effective learning and teaching-and thus progression and success-in higher education. While factors such as key introductory-level (CS1) knowledge and skills, as well as pre-university learning and qualifications, have been extensively explored, the impact of measures of positive psychology are less well understood for the discipline of computer science. University study can be a period of significant transition for many students; therefore an individual's positive psychology may have considerable impact upon their response to these challenges. This work investigates the relationships between effective learning and success (first-year performance and attendance) and two measures of positive psychology: Grit and the Nicolson McBride Resilience Quotient (NMRQ). Data was captured by integrating Grit (N=58) and Resilience (N=50) questionnaires and related coaching into the first-year of the undergraduate computer science programme at a single UK university. Analyses demonstrate that NMRQ is significantly linked to attendance and performance for individual subjects and year average marks; however, this was not the case for Grit. This suggests that development of targeted interventions to support students in further developing their resilience could support their learning, as well as progression and retention. Resilience could be used, in concert with other factors such as learning analytics, to augment a range of existing models to predict future student success, allowing targeted academic and pastoral support. CCS CONCEPTS • Social and professional topics → Computing education.

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Section: Discussionmentioning

confidence: 99%

Resilience and Effective Learning in First-Year Undergraduate Computer Science

Prickett

Walters

Yang

et al. 2020

Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education

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Section: Introductionmentioning

confidence: 99%

“…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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“…It is well established that introductory programming (CS1) courses in higher education have high dropout rates [9], [10]. A worldwide survey on pass rates reported that, on average, only 67% of students complete their CS1 course [11]. Watson & Li expanded on this study, finding that the mean pass rate of CS1 courses is 67.7% and that pass rates have not improved over time [12].…”

Section: A Dropout In Introductory Programming Coursesmentioning

confidence: 87%

Predicting abandonment in online coding tutorials

Yan

Lee

2017

2017 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)

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Learners regularly abandon online coding tutorials when they get bored or frustrated, but there are few techniques for anticipating this abandonment to intervene. In this paper, we examine the feasibility of predicting abandonment with machine-learned classifiers. Using interaction logs from an online programming game, we extracted a collection of features that are potentially related to learner abandonment and engagement, then developed classifiers for each level. Across the first five levels of the game, our classifiers successfully predicted 61% to 76% of learners who did not complete the next level, achieving an average AUC of 0.68. In these classifiers, features negatively associated with abandonment included account activation and help-seeking behaviors, whereas features positively associated with abandonment included features indicating difficulty and disengagement. These findings highlight the feasibility of providing timely intervention to learners likely to quit.

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Failure rates in introductory programming

Cited by 177 publications

References 10 publications

Resilience and Effective Learning in First-Year Undergraduate Computer Science

Resilience and Effective Learning in First-Year Undergraduate Computer Science

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

Predicting abandonment in online coding tutorials

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