Pedagogy of Programming Education for Higher Education Using Block Based Programming Environment

In this paper, we explore the challenges experienced by a group of Primary 5 to 6 (age 12–14) students as they engaged in a series of problem-solving tasks through block-based programming. The challenges were analysed according to a taxonomy focusing on the presence of computational thinking (CT) elements in mathematics contexts: preparing problems, programming, create computational abstractions, as well as troubleshooting and debugging. Our results suggested that the challenges experienced by students were compounded by both having to learn the CT-based environment as well as to apply mathematical concepts and problem solving in that environment. Possible explanations for the observed challenges stemming from differences between CT and mathematical thinking are discussed in detail, along with suggestions towards improving the effectiveness of integrating CT into mathematics learning. This study provides evidence-based directions towards enriching mathematics education with computation.

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Practice of Sandbox Game in Higher Education Based on Graphic and Game Programming Environment

Communications in Computer and Information Science - HCI International 2020 - Posters ◽

10.1007/978-3-030-50729-9_51 ◽

2020 ◽

pp. 356-364

Author(s):

Tengfei Xian

Keyword(s):

Higher Education ◽

Programming Environment ◽

Game Programming

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Students’ Behaviours in using Learning Resources in Higher Education: How do behaviours reflect success in Programming Education?

10.4995/head21.2021.12939 ◽

2021 ◽

Author(s):

Tai Tan Mai ◽

Martin Crane ◽

Marija Bezbradica

Keyword(s):

Higher Education ◽

Teaching And Learning ◽

Matrix Theory ◽

Statistical Tests ◽

Principal Component ◽

Learning System ◽

Drop Out ◽

Programming Education ◽

Depth Analysis ◽

Learning Behaviours

Programming education traditionally has been an important part of Information Technology-related degrees but, more recently, it is also becoming essential in many STEM domains as well. Despite this, drop-out rates in programming courses in higher education institutions are considerable and cannot be ignored. At the same time, analysing learning behaviours has been reported to be an effective way to support the improvement of teaching and learning quality. This article aims to deliver an in-depth analysis of students’ learning behaviours when using course material items. We analyse an introductory programming course at a University in Dublin. The dataset is extracted from automatically logged learning data from a bespoke online learning system. The analysis makes use of the power of Principal Component Analysis and Random Matrix Theory to reduce dimensionality in, and to extract information from, the data, verifying the results with rigorous statistical tests. Overall, we found that all the students follow a common learning pattern in accessing all given learning items. However, there is a noticeable difference between higher and lower-performing cohorts of students when using practical and theoretical learning items. The high performing students have been consistently active in practice during the study progress. On the other hand, the students who failed the exam have more recorded activities in reading lecture notes and appear to become discouraged and unmotivated from the practical activities, especially in the later stage of the semester.

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