Skip to main content
SpringerLink
Log in

Rule-Based Learner Competencies Predictor System

  • Conference paper
  • First Online:
Machine Intelligence for Research and Innovations (MAiTRI 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 832))

  • 42 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Denning PJ (2009) The profession of IT beyond computational thinking. Commun ACM 52(6):28–30

    Article  Google Scholar 

  2. Fanchamps N (2021) The influence of sense-reason-act programming on computational thinking. Open University, Heerlen

    Google Scholar 

  3. 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

    Article  Google Scholar 

  4. Papert S (1980) Mindstorms, children, computers and powerful ideas. Basic Books, inc.

    Google Scholar 

  5. Wing JM (2006) Computational thinking. Commun ACM 49(3):33–35. https://doi.org/10.1145/1118178.1118215

    Article  Google Scholar 

  6. 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

  7. 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

    Article  Google Scholar 

  8. Aldriye H, Alkhalaf A, Alkhalaf M (2019) Automated grading systems for programming assignments: a literature review. Int J Adv Comp Sci Appl 10(3)

    Google Scholar 

  9. 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

    Article  Google Scholar 

  10. 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

    Google Scholar 

  11. 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

    Article  Google Scholar 

  12. 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

    Article  Google Scholar 

  13. 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

    Article  Google Scholar 

  14. 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

    Article  Google Scholar 

  15. 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

    Article  Google Scholar 

  16. 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

  17. 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

    Article  Google Scholar 

  18. 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

    Article  Google Scholar 

  19. 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

  20. 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

    Article  Google Scholar 

  21. 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

    Article  Google Scholar 

  22. 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

  23. 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

    Article  Google Scholar 

  24. 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

    Article  Google Scholar 

  25. 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

    Article  Google Scholar 

  26. 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

  27. Wolf K, Stevens E (2007) The role of rubrics in advancing and assessing student learning. J Effect Teach 7(1):3–14

    Google Scholar 

  28. 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

    Google Scholar 

  29. 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

    Article  Google Scholar 

  30. 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

  31. 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

    Article  Google Scholar 

  32. 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

  33. 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

    Article  Google Scholar 

  34. 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

  35. 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

  36. 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

    Article  MathSciNet  Google Scholar 

  37. 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

    Article  Google Scholar 

  38. 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

    Article  Google Scholar 

  39. 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

    Article  Google Scholar 

  40. 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

  41. 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

    Article  Google Scholar 

  42. 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

    Article  Google Scholar 

  43. 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

    Article  Google Scholar 

  44. 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

    Article  Google Scholar 

  45. 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

    Article  Google Scholar 

  46. 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

  47. 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

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. AIIT, Amity University, Noida, Uttar Pradesh, India

    Priyanka Gupta

  2. ASET, Amity University, Noida, Uttar Pradesh, India

    Deepti Mehrotra

  3. University of Salford, Salford, UK

    Sunil Vadera

Authors
  1. Priyanka Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Deepti Mehrotra
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sunil Vadera
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Priyanka Gupta .

Editor information

Editors and Affiliations

  1. Dept of Instrumentation and Control Engg, Dr. B. R. Ambedkar National Institute of, Jalandhar, Punjab, India

    Om Prakash Verma

  2. School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore, Singapore

    Lipo Wang

  3. Department of Electrical Engineering, Malaviya National Institute of Technolog, Jaipur, Rajasthan, India

    Rajesh Kumar

  4. Department of Mathematics, DR BR Ambedkar NIT Jalandhar, Jalandhar, Punjab, India

    Anupam Yadav

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

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

Download citation

Publish with us

Policies and ethics

Search

Navigation