Reimagining the Singapore Pre-university Biology Curriculum with Computational Thinking

Reflections on Practice

Name:      Dr KOH Teck Seng
Address:  21 Nanyang Dr, Singapore 637371
Email:      kohteckseng@ntu.edu.sg

Name:      Dr SZE Chun Chau
Address:  60 Nanyang Dr, Singapore 637551
Email:      ccsze@ntu.edu.sg



Recommended Citation:
Chew, A. C. Y., Lee Y. X., Koh T. S., Sze C. C., & Goh, W. W. B. (2021). Reimagining the Singapore pre-university biology curriculum with computational thinking. Asian Journal of the Scholarship of Teaching and Learning, 11(2). 130-141.

View as PDF      Current Issue

Abstract

As the digital landscape expands and grows increasingly relevant, a worthwhile goal is to prepare and equip students with the necessary skills to thrive in such a society. Broadly known as computational thinking (CT), its wide reception is evident from educators’ efforts to incorporate it into national curricula worldwide. While some treat it as an independent discipline, this reflection argues that CT skills ought to be treated as an interdisciplinary skill. Using biology curricula as an example, we highlight the current gaps of integrating CT into biology through our teaching experiences at university level, which we believe stems from the descriptive nature that biology education has adopted in Singapore. With pre-university students seldom engaging in algorithmic approaches to analyse and understand biological data, we find that many students struggle to construct and test models using computational approaches at the university level. As such, we leverage on our experiences and propose a curriculum design at the pre-university level that embeds authentic application of CT into biological practice by mapping conceptual parallels between biology, computer science and CT. In the process, we detail elements of the curricula that are designed to cultivate deeper interdisciplinarity alongside the challenges that may be experienced in its setup. Envisioned to work in tandem with university courses, this works towards a first-point insertion for enabling future learners to understand more advanced digital literacies such as data analytics and machine learning that will be critical, if not commonplace, in the future field of biology.

Keywords: Computational thinking; biology; education; data science (DS); digital literacy

Introduction

As biology undergoes a technological revolution to produce unimaginable quantities of data, the need to analyse the consequently complex output will require computational strategies and ideas, whereby the future pursuit of biology will either require an interdisciplinary (requiring more than one expertise in an individual) or multidisciplinary (requiring different experts to work together) approach (Bentley, 2002) (see Table 1 under Appendix). Hence, it makes sense to develop the capacity for interdisciplinary and multidisciplinary work early on, amidst the multifaceted challenges of the 21st century. Computational thinking (CT) is one suitable problem-solving approach where learners engage with “the thought processes involved in formulating problems and their solutions so that the solutions are represented in a form that can be effectively carried out by an information-processing agent” (Wing 2006, 2008). The fundamental principles and concepts that underlie CT skills are also broad, allowing students to become divergent thinkers who can approach problems with different perspectives for the best solution (Wing, 2011). From the perspective of biology, CT provides a new way of interdisciplinary training to establish robust problem-solving and inquiry skills (Barr & Stephenson, 2011), allowing students to make meaningful connections across the various subfields and topics within biology (Pevzner & Shamir, 2009) and beyond. 

The significance of CT is acknowledged by educators in this growing information age through its incorporation into national curricula worldwide (Table 2), with some keeping CT skills as an independent discipline with separate curriculum time (e.g. Sweden and Taiwan), and others integrating CT skills into school curricula (e.g. Australia and South Korea). However, these implementation differences can result in varying learning outcomes, for example the ability of learners in using new technological skills (Hsu et al., 2018). Infusing CT skills across subjects has shown more effective and deeper learning (Bocconi et al., 2016). Mr Lawrence Wong (former Education Minister of Singapore) also highlighted interdisciplinary learning as one of the four main strategies to prepare students for a dynamic and uncertain future. The explosion of knowledge makes it important to have the ability to see the broader connection of things and to be able to work seamlessly across different disciplines. Many problems in the real-world are complex and cross-cutting, and cannot be solved with skills from a single discipline (Wong, 2020). 

In Singapore, the “Learn for Life” movement by the Ministry of Education (2020) has driven plans to instil CT into mathematics curricula. However, we argue that this needs to be extended into other disciplines, and in this reflection we use local biology curricula as an example. We find little reflection of CT within biology curricula in Singapore, and local pre-university biology remains traditionally taught as a didactic subject where students memorise large amounts of facts without really developing awareness of how bespoke facts were derived (Poon, 2014). With scientific terms presented as content knowledge to be memorised (Rosen & Julyan, 1989), we believe students miss out on the value and processes of scientific pursuit. Considering the prevalence of information technology contributing to the growing complexity of biological data, framing young minds with the thought processes to decode the data and troubleshooting skills is imperative in fostering better appreciation and understanding of biology (Mohaghegh & McCauley, 2016).  

Reflections on Current Courses in a University Context

At the School of Biological Sciences at Nanyang Technological University (NTU), our concrete effort to instil CT can be observed through our six integrative CT courses (Figure 1). Notably, they tap upon innovative pedagogies (Metz, 2008) such as flipped classroom models utilising NTU’s e-learning management platform, team-based learning (TBL), problem-based learning (PBL) alongside case studies and freestyle projects, which have also been described as being part of the 16 strategies for learners to apply CT skills (Hsu et al., 2018) and to induce deeper learning (Qin, 2009).

Given our experiences running these courses, we find that students did not completely appreciate the connections between biology and computing. This could be seen through introductory courses such as BS1009 “Introduction to Computational Thinking”, a compulsory course for freshmen, which have students locked in too early into the syntax of a single programming language instead of CT concepts such as iteration, nesting, loops and Boolean logic. We believe that the consequent struggle arises from a failure in applying concepts appropriately to problem sets, arising from a weak CT foundation. To circumvent fixation on mastery of the programming language, students are encouraged to learn and use whatever programming language they prefer in advanced data science courses such as BS3033 “Data Science for Biologists” to place more emphasis on problem solving. However, the drawback is that it becomes intensive to conduct, making such courses difficult to upscale and replicate. Furthermore, we realised the successes in the advanced course may stem from students who already have prior computing backgrounds and enrol based on their interest as compared to introductory courses which are compulsory for all first-year students. Overall, these experiences point to the need for stronger CT grounding amongst students, which we believe can be established earlier through strategic interventions in the pre-university arena.

Integrating Computational Thinking into Pre-university Biology Curriculum

To address these concerns, this Reflection presents a pre-university biology curricula design infused with CT, since an earlier introduction would prime students for solving problems at an earlier age (Qualls & Sherrell, 2010). While CT could be introduced as a standalone course, merely injecting CT into current biology curricula as yet another “add-on” or “extension topic” is inadequate, given the prevalence of bias and misconception regarding learning of biology by teachers and students at both pre-university and university levels (Qin, 2009). By decontextualising its practices, we run the risk of removing its authenticity when students attempt to apply it to real-world problems (Weintrop et al., 2015), inevitably leading to practice-theory gaps. It is hence worthwhile to leverage on the existing relationship between pre-university and university environments by developing a comprehensive CT programme in biology (Bio-CT for short). This could be done as an integrated two-tiered curriculum, with two strategic insertion points leading up to university (Figure 2)..

The first insertion point would be an introductory Bio-CT programme (iBio-CT) as a “plug-in” to pre-university biology curriculum (suitable for the last two years of high school before university), and the second, an optional advanced Bio-CT (aBio-CT) module for final-year high school students who show aptitude in the introductory programme. Together, Bio-CT seeks to embed CT principles within current biology concepts. This will not just draw interdisciplinary parallels between biological and computational fields, but also provide students with a foundation for future application of CT skills at higher levels. The teaching philosophy of embracing CT concepts as an integral component of biology will also update and sharpen existing biology educators’ interdisciplinary understanding (Wing, 2011).

In envisioning instructional material as a scaffold for both students and teachers, we used a five-cornerstone model to articulate the encompassing skills of CT (Figure 3). The model serves as a theoretical framework where acquired CT knowledge can be translated as biological inquiries embedded within real-world contexts. This is crucial, as relevant connections and applications are reportedly effective for demonstrating conceptual parallels between CT and biology (Kong & Abelson, 2019). 

Considering the parallels and connections between biology and CT may not be as clear-cut as the intuitive manifestations of CT within computer science (CS), we developed a relationship map within the three by drawing interdisciplinary parallels to CS manifestations (Figure 4).

Each biology topic can then be bridged to relevant CT cornerstones as illustrated in Table 3 (under Appendix), forming the basis of our proposed Bio-CT pre-university curriculum in Figure 1 (under Appendix). To further elaborate on what can happen during a lesson, we demonstrate an example of how HIV biology can be taught through posing a problem for students to discuss in groups, where they will apply CT skills to solve it collaboratively (Figure 5).

Overall, we envision instructional material in this Bio-CT course to be designed for self-paced learning, preferably online, with real-world problem-solving components and self-contained computational tools for hands-on practice. This approach encourages creativity and risk-taking in students, and also reduces teachers’ workload. Each lesson package will come with clear learning outcome assessments, structured easy-to-higher-level tutorial questions (all to be answered online with immediate feedback), freeing up the teachers’ time in looking for additional resources and grading assignments. Through their performances in mini-assessments and group problem-solving tasks set within iBio-CT, students can then gauge their aptitude and interest and opt for the aBio-CT module in their final year of the pre-university programme.

In line with advancing the 21st-century competencies in Singapore (Tan et al, 2017), we intend to generate student interest and curiosity in such courses and hope that they have the commitment to successfully complete them. As current Education Minister Mr Chan Chun Sing said, curiosity will propel student learning, and discipline enables the students to pursue, master, and perfect whatever they desire to learn (Ministry of Education, 2021).

Challenges and Opportunities

We imagine incorporating CT into biology will present growing pains. Due to differences in computer science and biological science learning traditions, important learning outcomes to a competent computer science student may not be applicable in the biology context (Wooley & Lin, 2005; Goth, 2010), the converse is also true. The challenge hence unfolds in adeptly navigating the tensions between the two traditions (Denning, 2017). Taking reported examples of Bio-CT curricula and approaches (Wilensky & Reisman, 2006; Libeskind-Hadas & Bush, 2013; Libeskind-Hadas & Bush, 2014; Rubinstein & Chor, 2014; Arraki et al., 2015; Burgett et al., 2015; Helikar et al., 2015; Orton et al., 2016; Mulder et al., 2018; Goh & Sze, 2019), alongside our experiences in designing and conducting Bio-CT and Physics-CT courses at NTU, we view the nature of implementing this curriculum as a translational one⸻distilling what has shown to work in one context (our university curricula) and implementing it within the pre-university context as a self-contained curricula package. 

Beyond implementation difficulties, it is important to recognise that its feasibility hinges on how palatable the courses are to students. For many, biology may be the field of choice for those wishing to pursue a science discipline which involves little mathematics (Wachsmuth et al., 2017), and such a fixed mindset along with a strong disinterest in math is challenging for our proposed Bio-CT curriculum (Grover et al., 2016). To circumvent these issues, participating students need to receive the appropriate messaging to manage their expectations and predispositions. Additionally, the development of CT elements should engage student interest, of which the Bebras site provides suitable examples (Bebras International Challenge of Informatics and Computational Thinking, 2021).

Lastly, a paradigm shift in thinking is also necessary for teachers as they are key stakeholders in fostering educational change (Villoutreix, 2021). To them, these ideologies may not translate into instructional practices due to many factors, be it internally through their own personal beliefs and commitment to the innovation (Tan & Nashon, 2017; Coenders et al., 2008), or externally through the lack of support and constraints to develop and integrate the new learning practices into the classroom (Lam et al., 2013). For example, school teachers in South Korea face great challenges when tasked to teach “software education”, due to their minimal training in computer science (Park, 2016). To overcome such attitudes and challenges, concerted effort expended in ensuring teachers have sufficient time and opportunity to experience and reflect upon CT problem-solving skills will increase their confidence (Leong et al., 2011). Leaving the practical details of implementation to the teachers’ own discretion to carry out the teachings have also shown success in Scandinavia (Kristensen, 2020), increasing their sense of ownership and autonomy in ensuring the programme’s success and sustainability. 

Implementing curriculum changes is no easy feat, and constant discussions amongst all stakeholders will be necessary to ensure adequate support is provided on the ground in terms of resource allocation and training. Whether this change has a definite positive impact, it also needs timely monitoring and continued systematic analysis (So et al., 2020). Future work in developing appropriate assessment measures (didactic, portfolio, interview etc.) that can bolster personal engagement will also be vital for ensuring the curriculum’s success (Grover & Pea, 2013; Klement, 2020).

Conclusion

As we seek to prepare future students for the rich opportunities of the information age, we understand that developing computational thinking early on is difficult but an imperative. We propose the insertion of CT concepts into biology curriculum for both pre-university and university levels. In doing so, we wish to break down the structural barriers that inhibit interdisciplinary discussion while challenging current perceptions to education. Moving towards learning for life, this reflection hopes to reframe education as a space for skills development and not merely an information delivery system.

Authors' Contributions

ACYC and LYX wrote the manuscript with input from all authors. KTS, CCS and WWBG conceptualised the biology and computational thinking curriculum. WWBG supervised the writing of the manuscript. All the authors have read and approved the manuscript.

Acknowledgements

WWBG and CCS gratefully acknowledge support from an Accelerating Creativity and Excellence (ACE) and an EdeX grant from the Nanyang Technological University, Singapore.

Appendix

References

Arraki, K., Blair, K., Burgert, T., Greenling, J., Haebe, J., Lee, G., … Hug, S. (2014). DISSECT: An experiment in infusing computational thinking in K-12 science curricula. 2014 IEEE Frontiers in Education Conference (FIE) Proceedings. https://doi.org/10.1109/fie.2014.7044262 

Barr, V., & Stephenson, C. (2011). Bringing computational thinking to K-12. ACM Inroads, 2(1), 48-54. https://doi.org/10.1145/1929887.1929905   

Bebras International Challenge on Informatics and Computational Thinking (2021, April 21).  Bebras Task Examples. https://www.bebras.org/examples.html

Bell, T., Andreae, P., & Robins, A. (2014). A case study of the introduction of computer science in NZ schools. ACM Transactions on Computing Education, 14(2), 1–31. https://doi.org/10.1145/2602485.

Bentley, P. J. (2002). Why biologists and computer scientists should work together. Lecture Notes in Computer Science, 3–15. https://doi.org/10.1007/3-540-46033-0_1 

Bocconi, S., Chioccariello, A., Dettori, G., Ferrari, A., & Engelhardt, K. (2016). Developing computational thinking in compulsory education–Implications for policy and practice; EUR 28295 EN; http://dx.doi.org/10.2791/792158

Burgett, T., Folk, R., Fulton, J., Peel, A., Pontelli, E., & Szczepanski, V. (2015). DISSECT: Analysis of pedagogical techniques to integrate computational thinking into K-12 curricula. 2015 IEEE Frontiers in Education Conference (FIE). https://doi.org/10.1109/fie.2015.7344241

Chen, J.-M., Wu, T.-T., & Sandnes, F. E. (2018). Exploration of computational thinking based on Bebras performance in Webduino programming by high school students. Lecture Notes in Computer Science, 443–452. https://doi.org/10.1007/978-3-319-99737-7_47 

Coenders, F., Terlouw, C., & Dijkstra, S. (2008). Assessing teachers’ beliefs to facilitate the transition to a new chemistry curriculum: What do the teachers want? Journal of Science Teacher Education, 19(4), 317–335. https://doi.org/10.1007/s10972-008-9096-5

Denning, P. J. (2017). Remaining trouble spots with computational thinking. Communications of the ACM. 60(6): 33–39. http://dx.doi.org/10.1145/2998438   

Falkner, K., Vivian, R., & Falkner, N. (2014). The Australian digital technologies curriculum: challenge and opportunity. In J. Whalley & D. D'Souza (Eds.). Proceedings of the Sixteenth Australasian Computing Education Conference. Volume 148 (ACE '14), Australian Computer Society, Inc., Darlinghurst, Australia, 3-12.

Goh, W. W. B., & Sze, C. C. (2019). AI paradigms for teaching biotechnology. Trends in Biotechnology, 37(1), 1–5. https://doi.org/10.1016/j.tibtech.2018.09.009 

Goth, G. (2010). CS and biology's growing pains. Communications of the ACM, 53(3), 13–15. https://doi.org/10.1145/1666420.1666427

Grover S. & Pea, R (2013). Computational thinking in K–12: A review of the state of the field. Educational Researcher, 42(1):38-43. https://doi.org/10.3102/0013189X12463051

Grover, S., Pea, R., & Cooper, S. (2016). Factors influencing computer science learning in middle school. Proceedings of the 47th ACM Technical Symposium on Computing Science Education (SIGCSE '16). Association for Computing Machinery, NY, USA, 552–557. https://doi.org/10.1145/2839509.2844564 

Helikar, T., Cutucache, C. E., Dahlquist, L. M., Herek, T. A., Larson, J. J., & Rogers, J. A. (2015). Integrating interactive computational modeling in biology curricula. PLOS Computational Biology, 11(3). https://doi.org/10.1371/journal.pcbi.1004131 

Heintz, F., Mannila, L., Nordén, L.-Å., Parnes, P., & Regnell, B. (2017). Introducing programming and digital competence in Swedish K-9 education. Informatics in Schools: Focus on Learning Programming, 117–128. https://doi.org/10.1007/978-3-319-71483-7_10 

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. Computers & Education, 126, 296–310. https://doi.org/10.1016/j.compedu.2018.07.004 

Klement, Milan & Dragon, Tomáš & Bryndová, Lucie. (2020). Computational thinking and how to develop it in the educational process. http://dx.doi.org/10.5507/pdf.20.24457963

Kong, S. C., & Abelson, H. (Eds) (2019). Computational thinking education. Springer, Singapore. https://doi.org/10.1007/978-981-13-6528-7

Kong, S. C., Sheldon, J., & Li, K. Y. (Eds.). (2017). Conference Proceedings of International Conference on Computational Thinking Education 2017. Hong Kong: The Education University of Hong Kong.

Kristensen, K. (2020). Implementation of computational thinking in school curriculums across Asia. Communications in Computer and Information Science, 269–276. https://doi.org/10.1007/978-3-030-50729-9_38   

Lam, C. C., Alviar-Martin, T., Adler, S. A., & Sim, J. B.Y. (2013). Curriculum integration in Singapore: Teachers' perspectives and practice. Teaching and Teacher Education, 31, 23–34. https://doi.org/10.1016/j.tate.2012.11.004  

Leonelli, S. (2014). What difference does quantity make? On the epistemology of Big Data in biology. Big Data & Society. https://doi.org/10.1177/2053951714534395

Leong, Y. H., Dindyal, J., Toh, T. L., Quek, K. S., Tay, E. G., & Lou, S. T. (2011). Teacher preparation for a problem-solving curriculum in Singapore. Zdm, 43(6-7), 819–831. https://doi.org/10.1007/s11858-011-0356-z

Li, W. L., Hu, C. F., & Wu, C. C. (2016). Teaching high school students computational thinking with hands-on activities. ITiCSE 2016-Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education (Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE; 11-13-July-2016. Association for Computing Machinery. https://doi.org/10.1145/2899415.2925496 

Libeskind-Hadas, R., & Bush, E. (2013). A first course in computing with applications to biology. Briefings in Bioinformatics, 14(5), 610–617. https://doi.org/10.1093/bib/bbt005

Libeskind-Hadas, R., & Bush, E. C. (2014). Computing for biologists: Python programming and principles (1st ed.). Cambridge University Press.

May, R. M. (2004). Uses and abuses of mathematics in biology. Science, 303(5659), 790–793. https://doi.org/10.1126/science.1094442 

Metz, A. M. (2008). Teaching statistics in biology: Using inquiry-based learning to strengthen understanding of statistical analysis in biology laboratory courses. CBE—Life Sciences Education, 7(3), 317–326. https://doi.org/10.1187/cbe.07-07-0046 

Ministry of Education. (2020, March 4). Learn for life – ready for the future: Refreshing our curriculum and skillsfuture for educators. [Press release]. Retrieved from https://www.moe.gov.sg/news/press-releases/20200304-learn-for-life-ready-for-the-future-refreshing-our-curriculum-and-skillsfuture-for-educators.

Ministry of Education. (2021, June 2). Speech by Minister for Education Mr Chan Chun Sing at Teachers’ Conference and Excel Fest 2021. [Press release/ news on Speech]. Retrieved from https://www.moe.gov.sg/news/speeches/20210602-speech-by-minister-for-education-mr-chan-chun-sing-at-teachers-conference-and-excel-fest-2021.

Mohaghegh, M. & McCauley, M. (2016). Computational thinking: The skill set of the 21st century. International Journal of Computer Science and Information Technologies. 7(3): 1524-1530. 

Mulder, N., Schwartz, R., Brazas, M. D., Brooksbank, C., Gaeta, B., Morgan, S. L., … Welch, L. (2018). The development and application of bioinformatics core competencies to improve bioinformatics training and education. PLOS Computational Biology, 14(2). https://doi.org/10.1371/journal.pcbi.1005772 

Park, R. K. E. (2016). Preparing students for South Korea’s creative economy: The successes and challenges of education reform. Asia Pacific Foundation of Canada. Retrieved on 5 April 2021, from https://www.asiapacific.ca/sites/default/files/filefield/south_korea_education_report_updated.pdf.

Pevzner, P., & Shamir, R. (2009). Computing has changed biology—Biology education must catch up. Science, 325(5940), 541–542. https://doi.org/10.1126/science.1173876

Poon, C.-L. (2014). Five decades of science education in Singapore. Education Innovation Series, 1–25. https://doi.org/10.1007/978-981-4585-78-1_1

Qin, H. (2009). Teaching computational thinking through bioinformatics to biology students. Proceedings of the 40th ACM Technical Symposium on Computer Science Education - SIGCSE '09. https://doi.org/10.1145/1508865.1508932 

Qualls, J. A., & Sherrell, L. B. (2010). Why computational thinking should be integrated into the curriculum. Journal of Computing Sciences in Colleges. 25(5): 66–71. https://dl.acm.org/doi/10.5555/1747137.1747148

Rosen, W. G., & Julyan, C. L. (1989). Messing about in Science: Participation, Not Memorization. In High-school biology Today and Tomorrow: Papers presented at a Conference (pp. 184–196). National Academy Press.

Rubinstein, A., & Chor, B. (2014). Computational thinking in life science education. PLoS Computational Biology, 10(11). https://doi.org/10.1371/journal.pcbi.1003897 

Selby, C., & Woollard, J. (2013). Computational thinking: the developing definition.  https://eprints.soton.ac.uk/356481/ 

Seow, P., Looi, C. K., How, M.L., Wadhwa, B., & Wu, L.K. (2019). Educational policy and implementation of computational thinking and pogramming: Case study of Singapore. In: Kong SC., Abelson H. (eds) Computational Thinking Education. Springer, Singapore. https://doi.org/10.1007/978-981-13-6528-7_19

So, H. J., Jong, M. S. Y., & Liu, C. C. (2020). Computational thinking education in the Asian Pacific Region. The Asia-Pacific Education Researcher, 29(1), 1–8. https://doi.org/10.1007/s40299-019-00494-w  

Tan, J.P.L., Koh, E., Chan, M., Costes-Onishi, P. & Hung, D. (2017). Advancing 21st Century Competencies in Singapore. New York: Asia Society Centre for Global Education.

Tan, Y. S. M., & Nashon, S. M. (2013). Promoting teacher learning through learning study discourse: The case of science teachers in Singapore. Journal of Science Teacher Education, 24(5), 859–877. https://doi.org/10.1007/s10972-013-9340-5 

Villoutreix, P. (2021). What machine learning can do for developmental biology. Development, 148(1). https://doi:10.1242/dev.188474  

Wachsmuth, L. P., Runyon, C. R., Drake, J. M., & Dolan, E. L. (2017). Do biology students really hate math? Empirical insights into undergraduate life science majors’ emotions about mathematics. CBE—Life Sciences Education, 16(3). https://doi:10.1187/cbe.16-08-0248  

Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2015). Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology, 25(1), 127–147. https://doi.org/10.1007/s10956-015-9581-5

Wilensky, U., & Reisman, K. (2006). Thinking like a wolf, a sheep, or a firefly: Learning biology through constructing and testing computational theories—An embodied modeling approach. Cognition and Instruction, 24(2), 171–209. https://doi.org/10.1207/s1532690xci2402_1 

Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35. https://doi.org/10.1145/1118178.1118215  

Wing, J. (2008). Computational thinking and thinking about computing. 2008 IEEE International Symposium on Parallel and Distributed Processing. https://doi.org/10.1109/ipdps.2008.4536091 

Wing, J. M. (2011). Research Notebook: Computational Thinking— What and Why. Carnegie Mellon University School of Computer Science The Link e-Magazine. https://www.cs.cmu.edu/link/research-notebook-computational-thinking-what-and-why (accessed on 21 April 2021)

Wong, L. (2020). Four strategies to prepare students for the future. NUS115 Distinguished Speaker Series. National University of Singapore, Singapore. 3 December 2020. https://news.nus.edu.sg/education-minister-lawrence-wong-lays-out-four-strategies-to-prepare-students-for-the-future/ 

Wooley, J. C., & Lin, H. (2005). Catalyzing inquiry at the interface of computing and biology. National Academies Press.

Wilson GOH is the Assistant Chair (Graduate Education), at the School of Biological Sciences (SBS), and Assistant Professor of Biomedical Informatics, Lee Kong Chian School of Medicine, Nanyang Technological University (NTU). His research interests include biodata science and educational technology which pertains to the use of data analytics for analyzing student performance and the development of software for facilitating learning.

Wilson can be reached at wilsongoh@ntu.edu.sg.


KOH Teck Seng is a Senior Lecturer at the School of Physical and Mathematical Sciences, Nanyang Technological University (NTU). His research interests cover quantum computing as well as physics and science education.

Teck Seng can be reached at kohteckseng@ntu.edu.sg.


SZE Chun Chau is the Director of Experiential and Collaborative Learning at the Institute of Pedagogical Innovation, Research and Excellence and Assistant Chair (Undergraduate Education) at the School of Biological Sciences (SBS), Nanyang Technological University (NTU). Her research interests encompass research related to bacterial gene regulation, biofilm biology, microorganisms in environmental engineering systems, bacteriology-engineering interface and residential and experiential learning.

Chun Chau can be reached at ccsze@ntu.edu.sg.