The gap between knowledge and ability

Berges, Marc; Mühling, Andreas; Hubwieser, Peter

doi:10.1145/2401796.2401812

Cited by 11 publications

(11 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The students do not know how to determine the number of methods needed or what labels or names to assign to them. They do not understand how to reuse methods or their proper placement (Berges et al, 2012;Gorschek et al, 2010;Hubwieser & Mühling, 2011;Karahasanović et al, 2007;Moons & De Backer, 2009;Moussa et al, 2016;Olsson & Mozelius, 2015;Sajaniemi et al, 2007;Sanders et al, 2008;Sheetz et al, 1997;Tegarden & Sheetz, 2001). Difficulty in understanding, teaching and implementing object-orientation (D04).…”

Section: Identified Problemsmentioning

confidence: 99%

Ranking of problems and solutions in the teaching and learning of object-oriented programming

2022

View full text Add to dashboard Cite

This study describes the most relevant problems and solutions found in the literature on teaching and learning of object-oriented programming (OOP). The identification of the problem was based on tertiary studies from the IEEE Xplore, Scopus, ACM Digital Library and Science Direct repositories. The problems and solutions identified were ranked through the multi-criteria decision methods DEMATEL and TOPSIS in order to determine the best solutions to the problems found and to apply these results in the academic context. The main contribution of this study was the categorization of OOP problems and solutions, as well as the proposal of strategies to improve the problem. Among the most relevant problems it was found: 1) difficulty in understanding, teaching and implementing object-orientation, 2) difficulties related to understanding classes and 3) difficulty in understanding object-oriented relationships. After doing the multicriteria analysis, it was found that the most important solutions to face the problems found in the teaching of OOP were: 1) use of active learning techniques and intrinsic rewards and 2) emphasize on basic programming concepts and introduce the object-oriented paradigm at an early point in the curriculum. As a conclusion, it was evidenced that there is coherence between the literary guarantee that gives support to the problems and solutions in the teaching of OOP presented in this study and the approaches that experts in the area of development highlight as relevant when they identify weaknesses in the process.

show abstract

Section: Identified Problemsmentioning

confidence: 99%

Ranking of problems and solutions in the teaching and learning of object-oriented programming

2022

View full text Add to dashboard Cite

show abstract

“…In an intelligent tutoring system for programming courses, a student cognitive model has been often needed to describe students' cognitive states during their studying. Early research efforts in this field highlighted observable gaps between students' understanding of core programming concepts and their capability of applying these concepts to the construction of simple programs [6]. Therefore, modeling the learning process of novice students in programming courses involves describing the temporal development of multiple latent cognitive skills.…”

Section: Student Cognitive Modelmentioning

confidence: 99%

HELP-DKT: An Interpretable Cognitive Model of How Students Learn Programming Based on Deep Knowledge Tracing

Liang

Peng

et al. 2021

Preprint

View full text Add to dashboard Cite

Student cognitive models are playing an essential role in intelligent online tutoring for programming courses. These models capture students' learning interactions and store them in the form of a set of binary responses, thereby failing to utilize rich educational information in the learning process. Moreover, the recent development of these models has been focused on improving the prediction performance and tended to adopt deep neural networks in building the end-to-end prediction frameworks. Although this approach can provide an improved prediction performance, it may also cause difficulties in interpreting the student's learning status, which is crucial for providing personalized educational feedback. To address this problem, this paper provides an interpretable cognitive model named HELP-DKT, which can infer how students learn programming based on deep knowledge tracing. HELP-DKT has two major advantages. First, it implements a feature-rich input layer, where the raw codes of students are encoded to vector representations, and the error classifications as concept indicators are incorporated. Second, it can infer meaningful estimation of student abilities while reliably predicting future performance. The experiments confirm that HELP-DKT can achieve good prediction performance and present reasonable interpretability of student skills improvement. In practice, HELP-DKT can personalize the learning experience of novice learners.

show abstract

“…Based on a concept extraction from the materials of the underlying course, a list of 21 concepts was formed according to the method described in [3]: access modifier (AM), array (AR), assignment (AG), association (AC), attribute (AT), class (CL), conditional statement (CS), constructor (CO), data encapsulation (DE), datatype (DT), inheritance (IN), initialization (IS), instance (IT), loop statement (LO), method (ME), object (OB), object orientation (OO), operator (OP), overloading (OV), parameter (PA), and state (ST). For the final concept list, four of them are eliminated because of different reasons.…”

Section: Data Collectionmentioning

confidence: 99%

“…In [3] the concepts mentioned above, were split up into observable items in the code. These items cover all observable aspects that are related to a specific concept.…”

Section: Data Collectionmentioning

confidence: 99%

Evaluation of Source Code with Item Response Theory

Berges

Hubwieser

2015

Proceedings of the 2015 ACM Conference on Innovation and Technology in Computer Science Education

Self Cite

View full text Add to dashboard Cite

The analysis of source code produced by novice programmers could provide interesting insights into their learning progress, particularly in introductory programming courses. Yet, as the programming ability of a person is assumed to be quite complex, it is not likely that it would be observable directly in its total. Instead, we regard those abilities as latent psychometric constructs and apply the methodology of item response theory (IRT) to assess their manifestations. In preparatory work, we had identified a list of items that represent the central concepts of object-oriented programming. In this paper we propose a methodology that allows the evaluation of coding abilities by analyzing the application of those concepts. We demonstrate this methodology by exemplarily analyzing source code that was produced during programming projects. The results provide interesting information about the difficulty of the concepts' application and the distribution of the respective coding abilities among the students.

show abstract

The gap between knowledge and ability

Cited by 11 publications

References 23 publications

Ranking of problems and solutions in the teaching and learning of object-oriented programming

Ranking of problems and solutions in the teaching and learning of object-oriented programming

HELP-DKT: An Interpretable Cognitive Model of How Students Learn Programming Based on Deep Knowledge Tracing

Evaluation of Source Code with Item Response Theory

Contact Info

Product

Resources

About