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Developing Industry-Relevant Higher Order Thinking Skills in Computing Students

Published: 15 June 2020 Publication History

Abstract

A 2016 survey commissioned by the British Higher Education Council revealed CS graduates had one of the highest unemployment rates. Industry sources attribute this to a growing gap between employer expectations and university curriculum. This gap primarily consists of soft skills and higher-order thinking skills, which include the ability to analyse, synthesise, evaluate, critique and design. Technical job advertisements increasingly specify higher order thinking skills as entry requirements for graduates.
This paper presents the results of our work studying whether formative tasks can develop higher-order thinking skills. The formative tasks consisted of quizzes covering the first four levels of the Bloom's taxonomy. The higher-order thinking skills were measured based on the static and dynamic models students created for a real world problem, as part of the final exam. Our results reveal a strong correlation exists between application and analysis level quizzes and design activities needing higher-order thinking in the exam. Students repeating the quizzes several times also had better results than others in higher-order design skills. These results suggest that practising with well-constructed quizzes especially at the higher Bloom's level exercises the cognitive channels that help improve higher-order thinking skills.

References

[1]
A. Ahadi, R. Lister, H. Haapala, and A. Vihavainen. 2015. Exploring Machine Learning Methods to Automatically Identify Students in Need of Assistance. ICER(2015).
[2]
N. Almi, A. M. Ezza, N. A. Rahman, D. Purusothaman, and S. Sulaiman. 2011.Software engineering education: The gap between industry's requirements and graduates' readiness. In Computers Informatics (ISCI). IEEE.
[3]
D. Bennett, S. Richardson, and P. MacKinnon. 2016. Enacting strategies for graduate employability: How universities can best support students to develop generic skill Part A. (2016).
[4]
J. B. Biggs and K. F. Collis. 1982.Evaluating the quality of learning: The SOLO taxonomy (Structure of the Observed Learning Outcome). Academic Press, New York.
[5]
B. S. Bloom. 1956. Taxonomy of Educational Objectives Cognitive Domain.(1956).
[6]
R. Boden and M. Neveda. 2010. Employing discourse: Universities and graduate employability. Journal of Education Policy25 (2010). Issue 1.
[7]
R. Bridgstock. 2017. Graduate employability 2.0: The networked approach tograduate employability. (2017). http://www.graduateemployability2-0.com/
[8]
E. R. Burns. 2010. "Anatomizing" Reversed: Use of Examination Questions that Foster Use of Higher Order Learning Skills by Students. Anatomical ScienceEducation3, 6 (2010).
[9]
A. Carbone, M. Hamilton, and M. Jollands. 2015. Moving towards the future of teaching pedagogies and learning paradigms: understanding the 21st century employability challenges in the ICT industry. (2015). http://www.rwl2015.com/papers/Paper019.pdf
[10]
G. J. Cizek and M. B. Bunch. 2007. Standard stetting: A guide to establishing and evaluating performance standards on test. Sage.
[11]
Deloitte. 2015. Tech trends 2015: The fusion of business and IT. (2015). http://www2.deloitte.com/au/en/pages/technology/articles/tech-trends-2015.html
[12]
A. Eckerdal, M. Ratcliffe, R. Mccartney, J. E. Mostrom, and C. Zander. 2006. Can Graduating Students Design Software Systems? ACM SIGCSE Bulletin 38, 1(2006).
[13]
U. Fuller, C. Johnson, T. Ahoniemi, D. Cukierman, I. Hernán-Losada, J. Jackova,E. Lahtinen, T. Lewis, D. McGee Thompson, C. Riedesel, and E. Thompson. 2007. Developing a computer science-specific learning taxonomy. SIGCSE Bulletin39(2007), 152--170. Issue 4.
[14]
S. Hadjerrouit. 2005. Constructivism as Guiding Philosophy for Software Engineering Education.The SIGCSE Bulletin37, 4 (2005).
[15]
S. Hajkowicz, A. Reeson, L. Rudd, A. Bratanova, L. Hodgers, C. Mason, and N. Boughen. 2016. Tomorrow's Digitally Enabled Workforce: Megatrends and scenarios for jobs and employment in Australia over the coming twenty years. Data 61 | CSIRO Report, Brisbane(2016). https://doi.org/10.4225/08/58557df808f71
[16]
R. Helens-Hart. 2015. Employability and Empowerment: Discursive constructions of Career Planning. kuscholar works(2015). http://hdl.handle.net/1808/19044
[17]
S. Hickson, R. W. Reed, and N. Sander. 2012. Estimating the Effect on Grades of Using Multiple-Choice Versus Constructive-Response Questions: Data From the Classroom. (19) (PDF). Educational Assessment 17(4) (2012), 200--213.
[18]
R. W. Hollingworth and C. E. McLoughlin. 2005.Teaching in the Sciences Learner-Centered Approaches. Australian Journal of Educational Computing, Vol. 21. Taylor and Francis Group, New York.
[19]
J. D. Karpicke, A. C. Butler, and H. Roediger. 2009. Metacognitive strategies in student learning: do students practice retrieval when they study on their own? Memory 17, 4 (2009).
[20]
H. Khosravi and K. Cooper. 2017. Using learning analytics to investigate patterns of performance and engagement in large classes. SIGCSE(2017).
[21]
D.R. Krathwohl. 2002. A revision of Bloom's taxonomy: An overview. Theory into practice 41 (2002). Issue 4.
[22]
D. Laurillard. 2002.Rethinking university teaching. A conversational framework for the effective use of learning technologies. Taylor Francis, New York.
[23]
E. Lindsay. 2014. Employers Perspectives on Graduate Recruitment in Australia. Graduate outlook(2014), 6.
[24]
A. Majanoja and T. Vasankari. 2018. Reflections on Teaching Software Engineering Capstone Course. In International Conference on Computer Supported Education. SCITE, 68--77.
[25]
R. Moreno. 2004. Decreasing Cognitive Load for Novice Students: Effects of Explanatory versus Corrective Feedback in Discovery-Based Multimedia. Instructional Science 32, 1--2 (2004), 99--113.
[26]
M. Paasivaara, J. Vanhanen, V. Heikkilä, C. Lassenius, J. Itkonen, and E. Laukkanen. 2017. Do High and Low Performing Student Teams Use Scrum Differently in Capstone Projects? ICSE-SEET(2017).
[27]
PWC. 2016. 21st century minds: Accelerator program. (2016). http://www.pwc.com.au/stem.html
[28]
A. Radermacher, D. Walia, and D. Knudson. 2014. Investigating the Skill Gap between Graduating Students and Industry Expectations. In ICSE. ACM, 291--300.
[29]
S S. McKenzie, J. Coldwell-Neilson, and S. Palmer. 2017. Informing the career development of IT students by understanding their career aspirations and skill development action plans. Australian Journal of Career Development 26 (2017),14--23. Issue 1.
[30]
Rhashvinder Singh, Charanjit Singh, M. T. M. Tunku, Nor Mostafa, and Tarsem Singh. 2018. A Review of Research on the Use of Higher Order Thinking Skills to Teach Writing. International Journal of English Linguistics(2018).
[31]
C. Starr, B. Manaris, and R. H. Stalvey. 2008. Bloom's taxonomy revisited: Specifying assessable learning objectives in computer science. In ACM SIGCSE Bulletin.ACM SIGCSE Bulletin 40(1):261--265.
[32]
H. Tarmazdi, R R. Vivian, C. Szabo, K. Falkner, and N. Falkner. 2015. Using Learning Analytics to Visualise Computer Science Teamwork. In ITiCSE. ACM,165--170.
[33]
C. Thevathayan and M. Hamilton. 2015. Supporting Diverse Novice Programming Cohorts through Flexible and Incremental Visual Constructivist Pathways. In ACM Conference on Innovation and Technology in Computer Science Education. ACM, 296--301.
[34]
L. Thomas, A. Eckerdal, R. McCartney, J. E. Moström, K. Sanders, and C. Zander.2014. Graduating Students' Designs ---Through a Phenomenographic Lens.ICER(2014).
[35]
V. X. Wang. 2015.Handbook of Research on Learning Outcomes and Opportunities in the Digital Age. IGI Global, Florida.
[36]
D. Whitelock. 2007. Computer assisted formative assessment: Supporting students to become more reflective learners. In 8th International Conference on Computer Based Learning in Science. The Open University, 492--503.
[37]
S. A. Wind, M. Alemdar, J. A. Lingle, R. Moore, and A. Asilkalkan. 2019. Exploring student understanding of the engineering design process using distractor analysis. International Journal of STEM Education6 (4) (2019).

Cited By

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  • (2022)Metacognition and Self-Regulation in Programming Education: Theories and Exemplars of UseACM Transactions on Computing Education10.1145/348705022:4(1-31)Online publication date: 15-Sep-2022
  • (2021)Teacher's Perception towards Assessing of Higher Order Thinking Skills (HOTS) in Elementary SchoolsProceedings of the 5th International Conference on Learning Innovation and Quality Education10.1145/3516875.3516980(1-6)Online publication date: 4-Sep-2021

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    cover image ACM Conferences
    ITiCSE '20: Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education
    June 2020
    615 pages
    ISBN:9781450368742
    DOI:10.1145/3341525
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 15 June 2020

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    Author Tags

    1. computer science education
    2. employability
    3. higher order thinking skills
    4. intelligent tutoring systems

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    View all
    • (2022)Metacognition and Self-Regulation in Programming Education: Theories and Exemplars of UseACM Transactions on Computing Education10.1145/348705022:4(1-31)Online publication date: 15-Sep-2022
    • (2021)Teacher's Perception towards Assessing of Higher Order Thinking Skills (HOTS) in Elementary SchoolsProceedings of the 5th International Conference on Learning Innovation and Quality Education10.1145/3516875.3516980(1-6)Online publication date: 4-Sep-2021

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