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International Journal of Modern Computer Science and IT Innovations

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Structured Teaching Framework Focused on Beginner-Level Software Development Skills

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Dr. Andika Prasetyo 1 , Siti Rahmawati, M.Sc. 1 , Rizky Maulana 1 ,
4 Department of Computer Science and Innovation Faculty of Engineering and Technology Universitas Teknologi Nusantara Jakarta, Indonesia
4 Department of Software Engineering School of Computing and Digital Innovation Institut Teknologi Bandung Bandung, Indonesia
4 Department of Information Systems and Innovation Faculty of Computer Science Universitas Gadjah Mada Yogyakarta, Indonesia

Abstract

The increasing demand for software development skills has intensified the need for effective instructional frameworks tailored to novice learners. Despite the proliferation of programming education initiatives, beginner-level learners continue to face significant challenges, including cognitive overload, lack of motivation, misconceptions, and ineffective instructional design. This study proposes a structured teaching framework specifically designed to enhance beginner-level software development skills by integrating explicit instruction, motivational theories, cognitive learning principles, and active learning strategies.

Drawing upon established educational theories such as constructivism, explicit instruction models, and motivation frameworks including the ARCS model, this research synthesizes insights from prior studies to develop a comprehensive pedagogical structure. The framework addresses key dimensions of learning, including conceptual understanding, skill acquisition, error correction, and engagement. It incorporates guided instruction, scaffolded practice, formative assessment, and adaptive feedback mechanisms to mitigate common learning barriers identified in introductory programming contexts.

The methodology adopts a conceptual and analytical approach, integrating empirical findings from existing literature to construct a multi-phase teaching model. The proposed framework emphasizes the alignment between instructional strategies and learner cognitive processes, ensuring that beginners transition effectively from theoretical understanding to practical application. Additionally, it incorporates mechanisms to identify and correct misconceptions, a critical issue in programming education.

Findings indicate that structured, explicit, and motivation-driven teaching approaches significantly improve learner engagement, reduce error rates, and enhance conceptual clarity. The framework demonstrates potential applicability across diverse educational settings, including formal academic institutions and self-paced learning environments.

This research contributes to the field of computer science education by offering a systematic, theory-driven teaching model that addresses persistent challenges in beginner-level programming instruction. The study also highlights the importance of integrating cognitive, motivational, and instructional design principles to achieve effective learning outcomes. Future research may focus on empirical validation and adaptation of the framework across different learner populations and technological contexts.

Keywords

Structured Teaching Framework, Beginner Programming, Explicit Instruction, Software Development Education

References

📄 A. Altadmri and N. C. Brown, ‘37 million compilations: Investigating novice programming mistakes in large-scale student data’, in Proceedings of the 46th ACM Technical Symposium on Computer Science Education, 2015, pp. 522–527.
📄 V. L. Almstrum et al., ‘Concept inventories in computer science for the topic discrete mathematics’, in ACM SIGCSE Bulletin, 2006, vol. 38, pp. 132–145.
📄 A. L. Archer and C. A. Hughes, ‘Explicit Instruction: Effective and efficient teaching (what works for special-needs Learners)’, J. Spec. Educ., vol. 36, no. 4, pp. 186–205, 2011.
📄 M. Bocquillon et al., ‘Faut-il utiliser l’enseignement explicite en tout temps? Non... mais oui!’, Apprendre Enseigner Aujourd’hui, vol. 8, no. 2, pp. 25–28, 2019.
📄 D. Bédard et al., ‘Problem-based and project-based learning in engineering and medicine: determinants of students’ engagement and persistance’, Interdiscip. J. Probl.-Based Learn., vol. 6, no. 2, pp. 7–30, 2012.
📄 A. Carbonaro, ‘Good practices to influence engagement and learning outcomes on a traditional introductory programming course’, Interact. Learn. Environ., vol. 27, no. 7, pp. 919–926, 2019.
📄 L. Facey-Shaw et al., ‘Do badges affect intrinsic motivation in introductory programming students?’, Simul. Gaming, vol. 51, no. 1, pp. 33–54, 2020.
📄 J. M. Fletcher et al., Learning disabilities: From identification to intervention. Guilford Publications, 2018.
📄 A. Forte and M. Guzdial, ‘Computers for communication, not calculation: Media as a motivation and context for learning’, in 37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the, 2004, pp. 10-pp.
📄 C. Gauthier et al., ‘L’enseignement explicite’, 2007.
📄 G. L. Herman, ‘The development of a digital logic concept inventory’, PhD Thesis, University of Illinois at Urbana-Champaign, 2011.
📄 J. R. Hollingsworth and S. E. Ybarra, Explicit direct instruction (EDI): The power of the well-crafted, well-taught lesson. Corwin Press, 2009.
📄 E. Hong et al., ‘Effects of explicit instructions, metacognition, and motivation on creative performance’, Creat. Res. J., vol. 28, no. 1, pp. 33–45, 2016.
📄 R. Horváth and S. Javorskỳ, ‘New teaching model for Java programming subjects’, Procedia-Soc. Behav. Sci., vol. 116, pp. 5188–5193, 2014.
📄 C. Iriarte and S. Bayona, ‘IT projects success factors: a literature review’, Int. J. Inf. Syst. Proj. Manag., vol. 8, no. 2, pp. 49–78, 2020.
📄 H. C. Jiau et al., ‘Enhancing self-motivation in learning programming using game-based simulation and metrics’, IEEE Trans. Educ., vol. 52, no. 4, pp. 555–562, 2009.
📄 F. Johnson et al., ‘Experience Report: Identifying Unexpected Programming Misconceptions with a Computer Systems Approach’, in Proceedings of the 27th ACM Conference on on Innovation and Technology in Computer Science Education Vol. 1, 2022, pp. 325–330.
📄 F. Johnson et al., ‘Analysis of student misconceptions using Python as an introductory programming language’, in Proceedings of the 4th Conference on Computing Education Practice 2020, 2020, pp. 1–4.
📄 J. M. Keller, ‘Development and use of the ARCS model of instructional design’, J. Instr. Dev., vol. 10, no. 3, pp. 2–10, 1987.
📄 P. A. Kirschner et al., ‘Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching’, Educ. Psychol., vol. 41, no. 2, pp. 75–86, 2006.
📄 D. C. Leonard, Learning theories: A to Z. Bloomsbury Publishing USA, 2002.
📄 J. Libarkin, ‘Concept inventories in higher education science’, in BOSE Conf., 2008.
📄 K. Longi and others, ‘Exploring factors that affect performance on introductory programming courses’, 2016.
📄 A. Mbiada et al., ‘A Comparative Study of Computer Programming Challenges of Computing and Non-Computing First-Year Students’, Indonesia. J. Comput. Sci., vol. 12, no. 4, 2023.
📄 A. K. Mbiada et al., ‘Introductory Computer Programming Teaching and Learning Approaches: Review’, in 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET), 2022.
📄 G. Nel and L. Nel, ‘Motivational value of code. Org’s code studio tutorials in an undergraduate programming course’, in ICT Education: 47th Annual Conference of the Southern African Computer Lecturers’ Association, SACLA 2018, Gordon’s Bay, South Africa, June 18–20, 2018, Revised Selected Papers 47, 2019, pp. 173–188.
📄 Newmark, ‘Ten principles for great explicit teaching’.
📄 C. O’Malley and A. Aggarwal, ‘Evaluating the Use and Effectiveness of Ungraded Practice Problems in an Introductory Programming Course’, in Proceedings of the Twenty-Second Australasian Computing Education Conference, 2020, pp. 177–184.
📄 O. S. Pabón and L. M. Villegas, ‘Fostering motivation and improving student performance in an introductory programming course: An integrated teaching approach’, Rev. EIA, vol. 16, no. 31, pp. 65–76, 2019.
📄 J. Piaget, ‘Part I: Cognitive Development in Children–Piaget Development and Learning.’, J. Res. Sci. Teach., vol. 40, 2003.
📄 M. Pressley and K. R. Harris, ‘Cognitive strategies instruction: From basic research to classroom instruction’, J. Educ., vol. 189, no. 1–2, pp. 77–94, 2009.
📄 Y. Qian et al., ‘Teachers’ perceptions of student misconceptions in introductory programming’, J. Educ. Comput. Res., vol. 58, no. 2, pp. 364–397, 2020.
📄 A. L. Ribeiro et al., ‘Análise da Motivação em um Estudo Integrado de Programação Baseado em PBL’, in Anais do XXVI Workshop sobre Educação em Computação, 2018.
📄 R. M. Ryan and E. L. Deci, ‘Intrinsic and extrinsic motivations: Classic definitions and new directions’, Contemp. Educ. Psychol., vol. 25, no. 1, pp. 54–67, 2000.
📄 B. L. Santana et al., ‘Motivation of engineering students with a mixed-contexts approach to introductory programming’, in 2018 IEEE Frontiers in Education Conference (FIE), 2018, pp. 1–9.
📄 W. B. Schaufeli et al., ‘Utrecht work engagement scale-9’, Educ. Psychol. Meas., 2003.
📄 G. Sharma, ‘Computer Programming Comp103: Who Does Better?’, 2019.
📄 S. M. Souza and R. A. Bittencourt, ‘Motivation and engagement with PBL in an introductory programming course’, in 2019 IEEE Frontiers in Education Conference (FIE), 2019, pp. 1–9.
📄 K. S. Taber, Modeling learners and learning in science education. Springer, 2013.
📄 C. Taylor et al., ‘Computer science concept inventories: past and future’, Comput. Sci. Educ., vol. 24, no. 4, pp. 253–276, 2014.
📄 A. K. Veerasamy et al., ‘Identifying novice student programming misconceptions and errors from summative assessments’, J. Educ. Technol. Syst., vol. 45, no. 1, pp. 50–73, 2016.
📄 S. Wiedenbeck et al., ‘Factors affecting course outcomes in introductory programming.’, in PPIG, 2004, p. 11.
📄 Ž. Žanko et al., ‘Mediated transfer: impact on programming misconceptions’, J. Comput. Educ., pp. 1–26, 2022.
📄 Ž. Žanko et al., ‘Analysis of school students’ misconceptions about basic programming concepts’, J. Comput. Assist. Learn., vol. 38, no. 3, pp. 719–730, 2022.

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