Abstract:
Delivery of core programming principles to novices is a challenging task and many introductory
programming languages and platforms have been designed to support this process. Educational programming
languages generally focus on alleviating the syntax overhead enforced on novice learners by designing
languages with simple and concise keywords. Furthermore, only the most basic programming concepts
and principles are incorporated and many languages follow unique methods to provide more simplified
learning environments. However, considering the way programs are authored using these platforms, two
common contrasting approaches to program representation are identified as text-based and block-based
representations. Additionally, a hybrid approach of dual-modality interfaces, which combines the best of
both techniques has gained traction as a current trend in the development of educational programming
platforms. However, despite these extensive features, not all introductory programming languages can cater
to the exact requirements of novice learners and a dearth of comprehensive studies and literature reviews
have been conducted to investigate this context. This paper explores and presents a comprehensive review of
how different elements of educational programming languages and platforms contribute towards learning by
novices under the Technology Acceptance Model (TAM). The review is conducted under two main constructs
of TAM as (1) Perceived Usefulness (PU) and (2) Perceived Ease of Use (PEOU) and external factors
regarding the programming environment, language design, included programming concepts and supporting
features such as the target audience group, language extensibility, and availability of learning materials
are thoroughly investigated considering the typical behavioral patterns of novices concerning computer
programming education.
Citation:
Perera, P., Tennakoon, G., Ahangama, S., Panditharathna, R., & Chathuranga, B. (2021). A systematic review of introductory programming languages for novice learners. IEEE Access, 9, 88121–88136. https://doi.org/10.1109/ACCESS.2021.3089560