Abstract
Forecasting the academic achievement of students is a critical area of research in educational contexts. This domain's significance stems from its ability to develop efficient mechanisms that enhance academic outcomes and minimize student attrition. In this context, rubric-based progressive learning meticulously provides valuable insights into students’ preferences, knowledge, and competencies. This study proposes a recommender model for detecting the Computational Thinking (CT) competencies of programming learners using a rubric and machine learning. A programming rubric was prepared to cover key programming concepts. A quiz conducted afterward was scored as per the rubric designed. Hierarchical clustering was applied to the rubric scores of learners to segment them into four categories according to their learning parameters. The rules were generated as CT competencies using a rule-based classifier—a multiple-layer perceptron neural network, considering cluster categories as labels. The proposed model assists learners and instructors in identifying the learners’ learning capabilities and priorities, resulting in improved learner performances.
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References
Denning PJ (2009) The profession of IT beyond computational thinking. Commun ACM 52(6):28–30
Fanchamps N (2021) The influence of sense-reason-act programming on computational thinking. Open University, Heerlen
Nouri J, Zhang L, Mannila L, Norén E (2020) Development of computational thinking, digital competence and 21st century skills when learning programming in K-9. Educ Inq 11(1):1–17. https://doi.org/10.1080/20004508.2019.1627844
Papert S (1980) Mindstorms, children, computers and powerful ideas. Basic Books, inc.
Wing JM (2006) Computational thinking. Commun ACM 49(3):33–35. https://doi.org/10.1145/1118178.1118215
Hogenboom SA, Hermans FF, Van der Maas HL (2021) Computerized adaptive assessment of understanding of programming concepts in primary school children. Computer Science Education, 30. https://doi.org/10.1080/08993408.2021.1914461. https://www.tandfonline.com/action/showCitFormats? Accessed 22 Sept 2022
Stevens DD, Levi AJ (2023) Introduction to rubrics: an assessment tool to save grading time, convey effective feedback, and promote student learning. Routledge. https://doi.org/10.4324/9781003445432
Aldriye H, Alkhalaf A, Alkhalaf M (2019) Automated grading systems for programming assignments: a literature review. Int J Adv Comp Sci Appl 10(3)
Chowdhury F (2019) Application of rubrics in the classroom: a vital tool for improvement in assessment, feedback and learning. Int Educ Stud 12(1):61–68
Khoirom S, Sonia M, Laikhuram B, Laishram J, Singh TD (2020) Comparative analysis of Python and Java for beginners. Int Res J Eng Technol 7(8):4384–4407
Hsu T-C, Chang S-C, Hung Y-T (2018) How to learn and how to teach computational thinking: suggestions based on a review of the literature. Comput Educ 126:296–310. https://doi.org/10.1016/j.compedu.2018.07.004
Moon J, Do J, Lee D, Choi GW (2020) A conceptual framework for teaching computational thinking in personalized OERs. Smart Learn Environ 7(1):1–19. https://doi.org/10.1186/s40561-019-0108-z
Yang W, Ng DTK, Gao H (2022) Robot programming versus block play in early childhood education: effects on computational thinking, sequencing ability, and self-regulation. Br J Edu Technol 53(6):1817–1841. https://doi.org/10.1111/bjet.13215
Gabriele L, Bertacchini F, Tavernise A, Vaca-Cárdenas L, Pantano P, Bilotta E (2019) Lesson planning by computational thinking skills in Italian pre-service teachers. Inform Educ 18(1):69–104
Sun L, Hu L, Zhou D (2021) Which way of design programming activities is more effective to promote K-12 students’ computational thinking skills? A meta-analysis. J Comput Assist Learn 37(4):1048–1062. https://doi.org/10.1111/jcal.12545
Basu S, McElhaney KW, Rachmatullah A, Hutchins NM, Biswas G, Chiu J (2022) Promoting computational thinking through science-engineering integration using computational modeling. In Proceedings of the 16th International conference of the learning sciences-ICLS 2022. International Society of the Learning Sciences, pp. 743–750. https://doi.org/10.22318/icls2022.743
Castro LMC, Magana AJ, Douglas KA, Boutin M (2021) Analyzing students’ computational thinking practices in a first-year engineering course. IEEE Access 9:33041–33050. https://doi.org/10.1109/ACCESS.2021.3061277
De Souza AA, Barcelos TS, Munoz R, Villarroel R, Silva LA (2019) Data mining framework to analyze the evolution of computational thinking skills in game building workshops. IEEE Access 7:82848–82866. https://doi.org/10.1109/ACCESS.2019.2924343
Jeffrey RM, Lundy M, Coffey D, McBreen S, Martin-Carrillo A, Hanlon L (2022) Teaching computational thinking to space science students. arXiv preprint arXiv:2205.04416. https://doi.org/10.48550/arXiv.2205.04416
Tikva C, Tambouris E (2021) Mapping computational thinking through programming in K-12 education: a conceptual model based on a systematic literature review. Comput Educ 162:104083. https://doi.org/10.1016/j.compedu.2020.104083
Tang X, Yin Y, Lin Q, Hadad R, Zhai X (2020) Assessing computational thinking: a systematic review of empirical studies. Comput Educ 148:103798. https://doi.org/10.1016/j.compedu.2019.103798
Nkhoma C, Nkhoma M, Thomas S, Le NQ (2020) The role of rubrics in learning and implementation of authentic assessment: a literature review. In: Jones M (ed) Proceedings of InSITE 2020: informing science and information technology education conference. Informing Science Institute, pp 237–276. https://doi.org/10.28945/4606
Reddy MY (2011) Design and development of rubrics to improve assessment outcomes: a pilot study in a Master’s level business program in India. Qual Assur Educ 19(1):84–104. https://doi.org/10.1108/09684881111107771
Andrade H, Du Y (2005) Student perspectives on rubric-referenced assessment. Pract Assess Res Eval 10(1):3. https://doi.org/10.7275/g367-ye94
Panadero E, Jönsson A (2013) The use of scoring rubrics for formative assessment purposes revisited: a review. Educ Res Rev 9:129–144. https://doi.org/10.1016/j.edurev.2013.01.002
Sundeen TH (2014) Instructional rubrics: effects of presentation options on writing quality. Assess Writ 21:74–88. https://doi.org/10.1016/j.asw.2014.03.003
Wolf K, Stevens E (2007) The role of rubrics in advancing and assessing student learning. J Effect Teach 7(1):3–14
Company P, Contero M, Otey J, Camba JD, Agost M-J, Pérez-López D (2017) Web-based system for adaptable rubrics: case study on CAD assessment. Educ Technol Soc 20(3):24–41
Halonen JS, Bosack T, Clay S, McCarthy M, Dunn DS, Hill GW IV, McEntarffer R, Mehrotra C, Nesmith R, Weaver KA, Whitlock K (2003) A rubric for learning, teaching, and assessing scientific inquiry in psychology. Teach Psychol 30(3):196–208. https://doi.org/10.1207/S15328023TOP3003_01
Chandio MT, Pandhiani SM, Iqbal R (2016) Bloom's taxonomy: improving assessment and teaching-learning process. J Educ Educ Dev 3(2). https://doi.org/10.22555/joeed.v3i2.1034
Bhattacherjee S, Mukherjee A, Bhandari K, Rout AJ (2022) Evaluation of multiple-choice questions by item analysis, from an online internal assessment of 6th semester medical students in a rural medical college, West Bengal. Indian J Commun Med Offic Publ Indian Assoc Prev Soc Med 47(1):92–95. https://doi.org/10.4103/ijcm.ijcm_1156_21
Elgadal AH, Mariod AA (2021) Item analysis of multiple-choice questions (MCQs): assessment tool for quality assurance measures. Sudan J Med Sci 16(3):334–346. https://doi.org/10.18502/sjms.v16i3.9695
Das B, Majumder M, Phadikar S, Sekh AA (2021) Multiple-choice question generation with auto-generated distractors for computer-assisted educational assessment. Multimedia Tools Appl 80(21–23):31907–31925. https://doi.org/10.1007/s11042-021-11222-2
Burud I, Nagandla K, Agarwal P (2019) Impact of distractors in item analysis of multiple choice questions. Int J Res Med Sci 7(4):1136–1139. https://doi.org/10.18203/2320-6012.ijrms20191313
Abualigah LMQ (2019) Introduction. In: Feature selection and enhanced krill herd algorithm for text document clustering. Studies in computational intelligence, vol 816. Springer, Cham. https://doi.org/10.1007/978-3-030-10674-4_1
Ackermann MR, Blömer J, Kuntze D, Sohler C (2014) Analysis of agglomerative clustering. Algorithmica 69(1):184–215. https://doi.org/10.1007/s00453-012-9717-4
Weiss SM, Indurkhya N (1995) Rule-based machine learning methods for functional prediction. J Artif Intell Res 3:383–403. https://doi.org/10.1613/jair.199
Yedjour D (2020) Extracting classification rules from artificial neural network trained with discretized inputs. Neural Process Lett 52:2469–2491. https://doi.org/10.1007/s11063-020-10357-x
Desai M, Shah M (2021) An anatomization on breast cancer detection and diagnosis employing multilayer perceptron neural network (MLP) and convolutional neural network (CNN). Clinic eHealth. https://doi.org/10.1016/j.ceh.2020.11.002
Bui DT, Bui KTT, Bui QT, Van Doan C, Hoang ND (2017) Hybrid intelligent model based on least squares support vector regression and artificial bee colony optimization for time-series modeling and forecasting horizontal displacement of hydropower dam. In Handbook of neural computation. Academic Press, pp 279–293. https://doi.org/10.1016/B978-0-12-811318-9.00015-6
Tien Bui D, Tuan TA, Klempe H, Pradhan B, Revhaug I (2016) Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides 13(2):361–378. https://doi.org/10.1007/s10346-015-0557-6
Pham BT, Bui DT, Prakash I, Dholakia MB (2017) Hybrid integration of multilayer perceptron neural networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS. CATENA 149:52–63. https://doi.org/10.1016/j.catena.2016.09.007
Sadowski Ł, Hoła J, Czarnecki S, Wang D (2018) Pull-off adhesion prediction of variable thick overlay to the substrate. Autom Constr 85:10–23. https://doi.org/10.1016/j.autcon.2017.10.001
Ming Y, Qu H, Bertini E (2018) Rulematrix: visualizing and understanding classifiers with rules. IEEE Trans Visual Comput Graph 25(1):342–352. https://doi.org/10.1109/TVCG.2018.2864812
Gašević D, Dawson S, Rogers T, Gasevic D (2016) Learning analytics should not promote one size fits all: the effects of instructional conditions in predicting academic success. Internet High Educ 28:68–84. https://doi.org/10.1016/j.iheduc.2015.10.002
Rose S, Habgood J, Jay T (2017) An exploration of the role of visual programming tools in the development of young children’s computational thinking. Electron J E-Learn 15(4):297–309. http://www.ejel.org/volume15/issue4/p297
Shute VJ, Sun C, Asbell-Clarke J (2017) Demystifying computational thinking. Educ Res Rev 22:142–158. https://doi.org/10.1016/j.edurev.2017.09.003
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Gupta, P., Mehrotra, D., Vadera, S. (2024). Rule-Based Learner Competencies Predictor System. In: Verma, O.P., Wang, L., Kumar, R., Yadav, A. (eds) Machine Intelligence for Research and Innovations. MAiTRI 2023. Lecture Notes in Networks and Systems, vol 832. Springer, Singapore. https://doi.org/10.1007/978-981-99-8129-8_12
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