UPSI Digital Repository (UDRep)
Start | FAQ | About

QR Code Link :

Type :thesis
Subject :LB Theory and practice of education
Main Author :Mariam Nainan T.K.Nainan
Title :Visualised worked examples for learning introductory programming at tertiary level
Place of Production :Tanjong Malim
Publisher :Fakulti Seni, Komputeran dan Industri Kreatif
Year of Publication :2021
Corporate Name :Universiti Pendidikan Sultan Idris
PDF Guest :Click to view PDF file
PDF Full Text :Login required to access this item.

Abstract : Universiti Pendidikan Sultan Idris
The objectives of this study were to design and develop visualised worked examples for introductory  programming at tertiary level, evaluate their effectiveness compared to subgoal labelled worked  examples, explore students’ engagements with visualised worked examples, and explore students’  preferences and perceptions of the two types of worked examples. Quasi-experiment was conducted  with 87, 79, and 78 students in three sessions in an introductory programming course in a  foundation programme at a university  in  Selangor.  Test  data  were  collected  and  analysed   using  analysis  of covariance  and  chi  square  tests.  Students’  engagements  with  visualised   worked examples   were   observed   and   analysed   qualitatively.   Another   intervention   was  conducted with 38 students in undergraduate programmes from the same university, who  were   presented  both  types  of  worked  examples.  Questionnaire  data  were collected  and  analysed   quantitatively  and  qualitatively.  The  findings  of  this  study showed   no   significant    differences   in   effectiveness   for   knowledge   and   skill development  but,  for   programming  language  and  patterns  knowledge  development, pattern applications were  significantly associated with type of worked examples (χ²(2) = 16.48, p < .001; χ²(2) = 11.18, p = .004; χ²(1) = 5.07, p = .024). Also, students were engaged  with  visualised  worked  examples.  Additionally,  73.7%  of  the  students preferred  visualised worked examples and students perceived that visualised worked examples supported their  understanding in various aspects. The conclusion was that visualised worked examples were able to  significantly reduce the likelihood of wrong or omitted program statements in students’ pattern  applications. Also, students were engaged   with   visualised   worked   examples   behaviourally,    and   by   implication, cognitively. In addition, visualised worked examples were preferred by  more students with positive perceptions. The implications were that this study extended research on  worked example design, employing concepts of attention cueing and learner control, for  programming   education  and  provided  empirical  evidence  of  worked  examples usage for programming education practice.  

References

Al-Fraihat, D., Joy, M., Masa'deh, R., & Sinclair, J. (2020). Evaluating e-learning systems 

success: An empirical study. Computers in Human Behavior, 102, 67- 86. 

http://dx.doi.org/10.1016/j.chb.2019.08.004

 

Alhassan, R. (2017). The effect of employing self-explanation strategy with worked examples on 

acquiring computer programming skills. Journal of Education and Practice, 8(6), 186-196.

 

Allen, J. T., Bidjerano, T., & Hren, T. (2017). What do novices think about when they program?. 

Journal of Computing Sciences in Colleges, 33(2), 171-181.

 

Altintas, T., Gunes, A., & Sayan, H. (2016). A peer-assisted learning experience in computer 

programming language learning and developing computer programming skills. Innovations in Education 

and Teaching International, 53(3), 329-337. http://dx.doi.org/10.1080/14703297.2014.993418

 

Ambrose, S. A., Bridges, M. W., DiPietro, M., Lovett, M. C., & Norman, M. K. (2010). How learning 

works: Seven research-based principles for smart teaching. John Wiley & Sons.

 

Anderson, R. C., Spiro, R. J., & Anderson, M. C. (1978). Schemata as scaffolding for the 

representation of information in connected discourse. American Educational Research Journal, 15(3), 

433-440. http://doi.org/10.3102/00028312015003433

 

Atkinson, R. C., & Shiffrin, R. M. (1968). Human memory: A proposed system and its control 

processes. Psychology of Learning and Motivation, 2, 89-195. 

http://dx.doi.org/10.1016/S0079-7421(08)60422-3

 

Atkinson, R. K., Derry, S. J., Renkl, A., & Wortham, D. (2000). Learning from examples: 

Instructional principles from the worked examples research. Review of Educational Research, 70(2), 

181-214. http://dx.doi.org/10.3102/00346543070002181

 

Atkinson, R. K., & Renkl, A. (2007). Interactive example-based learning environments: Using 

interactive elements to encourage effective processing of worked examples. Educational Psychology 

Review, 19(3), 375-386. http://dx.doi.org/10.1007/s10648-007-9055-2

 

Bennedsen, J., & Caspersen, M. E. (2005, February). Revealing the programming process. In 

Proceedings of the 36th SIGCSE technical symposium on computer science education  (pp. 186-190). 

ACM.

https://doi.org/10.1145/1047344.1047413

 

Bennedsen, J., & Caspersen, M. E. (2007). Failure rates in introductory programming.

ACM SIGCSE Bulletin, 39(2), 32-36.

http://dx.doi.org/10.1145/1272848.1272879

 

Bennedsen, J., & Caspersen, M. E. (2019). Failure rates in introductory programming: 12 years 

later. ACM Inroads, 10(2), 30-36. http://dx.doi.org/10.1145/3324888

 

Bokosmaty, S., Sweller, J., & Kalyuga, S. (2015). Learning geometry problem solving by studying 

worked examples: Effects of learner guidance and expertise.

American Educational Research Journal, 52(2), 307-333. http://dx.doi.org/10.3102/0002831214549450

 

Busjahn, T., Bednarik, R., Begel, A., Crosby, M., Paterson, J. H., Schulte, C., ... & Tamm, S. 

(2015, May). Eye movements in code reading: Relaxing the linear order. In 2015 IEEE 23rd 

international conference on program comprehension (pp. 255-265). IEEE. 

http://dx.doi.org/10.1109/ICPC.2015.36

 

Busjahn, T., & Schulte, C. (2013, November). The use of code reading in teaching programming. In 

Proceedings of the 13th Koli calling international conference on computing education research (pp. 

3-11). ACM. http://doi.org/10.1145/2526968.2526969

 

Cardellini, L. (2014). Problem solving: How can we help students overcome cognitive difficulties. 

Journal of Technology and Science Education, 4(4), 237-249. http://dx.doi.org/10.3926/jotse.121

 

Carolan, T. F., Hutchins, S. D., Wickens, C. D., & Cumming, J. M. (2014). Costs and benefits of 

more learner freedom: Meta-analyses of exploratory and learner control training methods. Human 

Factors, 56(5), 999-1014. https://doi.org/10.1177/0018720813517710

 

Caspersen, M. E., & Bennedsen, J. (2007, September). Instructional design of a programming course: 

a learning theoretic approach. In Proceedings of the third international workshop on computing 

education research (pp. 111-122). ACM. https://doi.org/10.1145/1288580.1288595

 

Castro, F. E. V., & Fisler, K. (2016, February). On the interplay between bottom-up and 

datatype-driven program design. In Proceedings of the 47th ACM technical symposium on computing 

science education (pp. 205-210). ACM. http://dx.doi.org/10.1145/2839509.2844574

 

Catrambone, R. (1994). Improving examples to improve transfer to novel problems.

Memory & Cognition, 22(5), 606-615. http://doi.org/10.3758/BF03198399

 

Catrambone, R. (1996). Generalizing solution procedures learned from examples.

Journal of Experimental Psychology: Learning, Memory, and Cognition, 22(4), 1020-1031. 

http://doi.org/10.1037/0278-7393.22.4.1020

 

Catrambone, R. (1998). The subgoal learning model: Creating better examples so that students can 

solve novel problems. Journal of Experimental Psychology: General, 127(4), 355-376. 

http://doi.org/10.1037/0096-3445.127.4.355

 

Catrambone, R., & Holyoak, K. J. (1990). Learning subgoals and methods for solving probability 

problems. Memory & Cognition, 18(6), 593-603. http://doi.org/10.3758/BF03197102

 

Catrambone, R., & Yuasa, M. (2006). Acquisition of procedures: The effects of example elaborations 

and active learning exercises. Learning and Instruction, 16(2), 139-153. 

http://doi.org/10.1016/j.learninstruc.2006.02.002

 

Chen, J. C., Whittinghill, D. C., & Kadlowec, J. A. (2010). Classes that click: Fast, rich feedback 

to enhance student learning and satisfaction. Journal of Engineering Education, 99(2), 159-168. 

https://doi.org/10.1002/j.2168- 9830.2010.tb01052.x

 

Cherenkova, Y., Zingaro, D., & Petersen, A. (2014, March). Identifying challenging CS1 concepts in 

a large problem dataset. In Proceedings of the 45th ACM technical symposium on computer science 

education (pp. 695-700). ACM. http://doi.org/10.1145/2538862.2538966

 

Chi, M. T. H., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989). Self- explanations: How 

students study and use examples in learning to solve problems. Cognitive Science, 13(2), 145-182. 

https://doi.org/10.1016/0364-

0213(89)90002-5

 

Chow, W. S., & Shi, S. (2014). Investigating students’ satisfaction and continuance intention 

toward e-learning: An extension of the expectation–confirmation model. Procedia-Social and 

Behavioral Sciences, 141, 1145-1149. https://doi.org/10.1016/j.sbspro.2014.05.193

 

Clark, R. E., Kirschner, P. A., & Sweller, J. (2012). Putting students on the path to learning: The 

case for fully guided instruction. American Educator, 36(1), 6-11.

 

Cohen, L., Manion, L., & Morrison, K. (2018). Research methods in education (8th ed.). Routledge.

 

Crooks, S. M., Cheon, J., Inan, F., Ari, F., & Flores, R. (2012). Modality and cueing in multimedia 

learning: Examining cognitive and perceptual explanations for the modality effect. Computers in 

Human Behavior, 28(3), 1063-1071.

https://doi.org/10.1016/j.chb.2012.01.010

 

Crow, T., Luxton-Reilly, A., & Wuensche, B. (2018, January). Intelligent tutoring systems for 

programming education: a systematic review. In Proceedings of the 20th Australasian computing 

education conference (pp. 53-62). http://dx.doi.org/10.1145/3160489.3160492

 

Da?han, G., & Akkoyunlu, B. (2016). Modeling the continuance usage intention of online learning 

environments. Computers in Human Behavior, 60, 198-211. https://doi.org/10.1016/j.chb.2016.02.066

 

Deuter, M., Bradbery, J., Turnbull, J., Hey, L., &  Holloway, S. (Eds.). (2015).

Visualize. In Oxford Advanced Learner’s Dictionary of Current English (9th ed.). Oxford University 

Press

 

De Barros, L. N., dos Santos Mota, A. P., Delgado, K. V., & Matsumoto, P. M. (2005, October). A 

tool for programming learning with pedagogical patterns. In Proceedings of the 2005 OOPSLA workshop 

on eclipse technology exchange (pp. 125-129). ACM. http://doi.org/10.1145/1117696.1117722

 

De Jong, T. (2010). Cognitive load theory, educational research, and instructional design: Some 

food for thought. Instructional Science, 38(2), 105-134. http://doi.org/10.1007/s11251-009-9110-0

 

De Koning, B. B., Tabbers, H. K., Rikers, R. M. J. P., & Paas, F. (2007). Attention cueing as a 

means to enhance learning from an animation. Applied Cognitive Psychology, 21(6), 731-746. 

http://doi.org/10.1002/acp.1346

 

De Koning, B. B., Tabbers, H. K., Rikers, R. M. J. P., & Paas, F. (2009). Towards a framework for 

attention cueing in instructional animations: Guidelines for research and design. Educational 

Psychology Review, 21(2), 113-140. http://doi.org/10.1007/s10648-009-9098-7

 

De Raadt, M., Watson, R., & Toleman, M. (2009, January). Teaching and assessing programming 

strategies explicitly. In Proceedings of the eleventh Australasian conference on computing 

education (Vol. 95, pp. 45-54). Australian Computer Society Inc..

 

De Souza, D. M., Maldonado, J. C., & Barbosa, E. F. (2011, May). ProgTest: An environment for the 

submission and evaluation of programming assignments based on testing activities. In 2011 24th 

IEEE-CS conference on software engineering education and training (CSEE&T) (pp. 1-10). IEEE.

 

Deek, F. P., Turoff, M., & McHugh, J. a. (1999). A common model for problem solving and program 

development. IEEE Transactions on Education, 42(4), 331-

336. http://doi.org/10.1109/13.804541

 

Denscombe, M. (2010). Ground rules for social research: guidelines for good

practice. Open University Press.

 

Derry, J. (2007). Epistemology and conceptual resources for the development of learning 

technologies. Journal of Computer Assisted Learning, 23(6), 503-510. 

http://doi.org/10.1111/j.1365-2729.2007.00246.x

 

Domagk, S., Schwartz, R. N., & Plass, J. L. (2010). Interactivity in multimedia learning: An 

integrated model. Computers in Human Behavior, 26(5), 1024- 1033. 

http://doi.org/10.1016/j.chb.2010.03.003

 

dos Santos, M. T., Vianna Jr, A. S., & Le Roux, G. A. (2018). Programming skills in the industry 

4.0: are chemical engineering students able to face new problems? Education for Chemical Engineers, 

22, 69-76. https://doi.org/10.1016/j.ece.2018.01.002

 

Duran, R., Sorva, J., & Leite, S. (2018, August). Towards an analysis of program complexity from a 

cognitive perspective. In Proceedings of the 2018 ACM conference on international computing 

education research (pp. 21-30). ACM. https://doi.org/10.1145/3230977.3230986

 

East, J. P., Thomas, R., Wallingford, E., Beck, W., & Drake, J. (1996, June). Pattern based 

programming instruction. [Paper presentation] 1996 American Society for Engineering Education 

(ASEE) Annual Conference, Washington, USA, 1-10.

 

Elo, S., & Kyngäs, H. (2008). The qualitative content analysis process. Journal of Advanced 

Nursing, 62(1), 107-115. https://doi.org/10.1111/j.1365- 2648.2007.04569.x

 

Fernández Alemán, J. L., & Oufaska, Y. (2010, June). SAMtool, a tool for deducing and implementing 

loop patterns. In Proceedings of the fifteenth annual conference on innovation and technology in 

computer science education (pp. 68- 72). ACM. http://doi.org/10.1145/1822090.1822111

 

Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). SAGE Publications.

 

Fiorella, L., & Mayer, R. E. (2016). Eight ways to promote generative learning.

Educational Psychology Review, 28(4), 717-741. http://doi.org/10.1007/s10648-

015-9348-9

 

Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the 

concept, state of the evidence. Review of Educational

Research, 74(1), 59-109. https://doi.org/10.3102/00346543074001059

 

Garner, S., Haden, P., & Robins, A. (2005, January). My program is correct but it doesn’t run: A 

preliminary investigation of novice programmers' problems. In Proceedings of the 7th Australasian 

conference on computing education, (Vol 42, pp.173-180). Australian Computer Society, Inc..

 

Gaspar, A., & Langevin, S. (2007, October). Restoring “coding with intention” in introductory 

programming courses. In Proceedings of the 8th ACM SIGITE conference on information technology 

education (pp. 91-98). ACM. http://doi.org/10.1145/1324302.1324323

 

Gerjets, P., Scheiter, K., & Catrambone, R. (2004). Designing instructional examples to reduce 

intrinsic cognitive load: Molar versus modular presentation of solution procedures. Instructional 

Science, 32(1-2), 33-58. https://doi.org/10.1023/B:TRUC.0000021809.10236.71

 

Ginat, D. (2007, June). Hasty design, futile patching and the elaboration of rigor. In Proceedings 

of the 12th annual SIGCSE conference on innovation and technology in computer science education 

(pp. 161-165). ACM. http://doi.org/10.1145/1269900.1268832

 

Gomes, A., & Mendes, A. J. (2007, September). Learning to program-difficulties and solutions. 

International Conference on Engineering Education, European Journal of Engineering Education, 

32(1), 109. https://doi.org/10.1080/03043790601135014

 

Graneheim, U. H., Lindgren, B. M., & Lundman, B. (2017). Methodological challenges in qualitative 

content analysis: A discussion paper. Nurse Education Today, 56, 29-34. 

https://doi.org/10.1016/j.nedt.2017.06.002

 

Gray, S., St. Clair, C., James, R., & Mead, J. (2007, September). Suggestions for graduated 

exposure to programming concepts using fading worked examples. In Proceedings of the third 

international workshop on computing education research (pp. 99-110). ACM. 

https://doi.org/10.1145/1288580.1288594

 

Greene, J., Moos, D., & Azevedo, R. (2011). Self-regulation of learning with computer-based 

learning environments. New Directions for Teaching and Learning, 2011(126), 107-115. 

https://doi.org/10.1002/tl.449

 

Griffin, J. M. (2016, September). Learning by taking apart: Deconstructing code by reading, 

tracing, and debugging. In Proceedings of the 17th annual conference on information technology 

education (pp. 148-153). ACM. https://doi.org/10.1145/2978192.2978231

 

Grover, S., & Basu, S. (2017, March). Measuring student learning in introductory block-based 

programming: Examining misconceptions of loops, variables, and

boolean logic. In Proceedings of the 2017 ACM SIGCSE technical symposium on

computer science education (pp. 267-272). ACM. https://doi.org/10.1145/3017680.3017723

 

Gusukuma, L., Bart, A. C., Kafura, D., & Ernst, J. (2018, August). Misconception- driven feedback: 

Results from an experimental study. In Proceedings of the 2018 ACM conference on international 

computing education research (pp. 160-168). ACM. https://doi.org/10.1145/3230977.3231002

 

Guzdial, M., Hohmann, L., Konneman, M., Walton, C., & Soloway, E. (1998).

Supporting programming and learning-to-program with an integrated CAD and scaffolding workbench. 

Interactive Learning Environments, 6(1-2), 143-179. http://doi.org/10.1076/ilee.6.1.143.3609

 

Hanks, B., & Brandt, M. (2009, March). Successful and unsuccessful problem solving approaches of 

novice programmers. In Proceedings of the 40th ACM technical symposium on computer science 

education (pp. 24-28). ACM. http://doi.org/10.1145/1508865.1508876

 

Harskamp, E., & Suhre, C. (2007). Schoenfeld’s problem solving theory in a student controlled 

learning environment. Computers & Education, 49(3), 822-839. 

http://doi.org/10.1016/j.compedu.2005.11.024

 

Heinonen, K., Hirvikoski, K., Luukkainen, M., & Vihavainen, A. (2014, March).

Using CodeBrowser to seek differences between novice programmers.

In Proceedings of the 45th ACM technical symposium on computer science education (pp. 229-234). 

ACM. https://doi.org/10.1145/2538862.2538981

 

Hesser, T. L., & Gregory, J. L. (2015). Exploring the use of faded worked examples as a problem 

solving approach for underprepared students. Higher Education Studies, 5(6), 36-46. 

http://dx.doi.org/10.5539/hes.v5n6p36

 

Hohn, R. L., & Moraes, I. (1998). Use of rule-based elaboration of worked examples to promote the 

acquisition of programming plans. Journal of Computer Information Systems, 38(2), 35-40.

 

Hoy, A. W. (2013). Educational psychology (12th ed.). Pearson.

 

Hu, M., Winikoff, M., & Cranefield, S. (2013, January). A process for novice programming using 

goals and plans. In Proceedings of the fifteenth Australasian computing education conference (Vol. 

136, pp. 3-12).

 

Huang, T. C. (2019). Do different learning styles make a difference when it comes to

creativity? An empirical study. Computers in Human Behavior, 100, 252-257.

 

Ichinco, M., & Kelleher, C. (2015, October). Exploring novice programmer example use. In 2015 IEEE 

symposium on visual languages and human-centric computing (VL/HCC) (pp. 63-71). IEEE.  

https://ieeexplore.ieee.org/document/7357199

 

Izu, C., Weerasinghe, A., & Pope, C. (2016, August). A study of code design skills in novice 

programmers using the SOLO taxonomy. In Proceedings of the 2016 ACM conference on international 

computing education research (pp. 251-259). ACM. http://dx.doi.org/10.1145/2960310.2960324

 

Jamet, E., & Fernandez, J. (2016). Enhancing interactive tutorial effectiveness through visual 

cueing. Educational Technology Research and Development, 64(4), 631-641. 

https://doi.org/10.1007/s11423-016-9437-6

 

Jamet, E., Gavota, M., & Quaireau, C. (2008). Attention guiding in multimedia learning. Learning 

and Instruction, 18(2), 135-145. http://doi.org/10.1016/j.learninstruc.2007.01.011

 

Jenkins, T. (2002, August). On the difficulty of learning to program. In Proceedings of the 3rd 

annual conference of the LTSN centre for information and computer sciences (pp. 53-58).

 

Jenkinson, J. (2009). Measuring the effectiveness of educational technology?: what are we 

attempting to measure?. Electronic Journal of E-Learning, 7(3), 273-280. 

http://www.ejel.org/volume7/issue3

 

Johnson, B. and Christensen, L. (2014) Educational research: Quantitative, qualitative, and mixed 

approaches (5th ed.). SAGE Publications.

 

Joint Task Force on Computing Curricula (2015). Curriculum guidelines for undergraduate degrees in 

software engineering. Computing Curricula Series. ACM.

 

Jonassen, D. H. (2003). Designing research-based instruction for story problems.

Educational Psychology Review, 15(3), 267-296. http://doi.org/10.1023/A:1024648217919

 

Jonassen, D. H. (2010, September). Research issues in problem solving. In The 11th International 

conference on education research: New education paradigm for learning and instruction (pp. 1-15).

 

Jung, E., Kim, D., Yoon, M., Park, S., & Oakley, B. (2019). The influence of instructional design 

on learner control, sense of achievement, and perceived effectiveness in a supersize MOOC course. 

Computers & Education, 128, 377-

388. https://doi.org/10.1016/j.compedu.2018.10.001

 

Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). The expertise reversal

effect. Educational Psychologist, 38(1), 23-31. http://doi.org/10.1207/S15326985EP3801_4

 

Kalyuga, S., & Singh, A. M. (2016). Rethinking the boundaries of cognitive load theory in complex 

learning. Educational Psychology Review, 28(4), 831-852. 

http://dx.doi.org/10.1007/s10648-015-9352-0

 

Kay, J., Barg, M., Fekete, A., Greening, T., Hollands, O., Kingston, J. H. & Crawford,

K. (2000). Problem-based learning for foundation computer science courses. Computer Science 

Education, 10(2), 109-128. http://dx.doi.org/10.1076/0899- 3408(200008)10:2;1-C;FT109

 

Kim, A. S., & Ko, A. J. (2017, March). A pedagogical analysis of online coding tutorials. In 

Proceedings of the 2017 ACM SIGCSE technical symposium on computer science education (pp. 321-326). 

http://dx.doi.org/10.1145/3017680.3017728

 

Kinnunen, P., & Simon, B. (2010, August). Experiencing programming assignments in CS1: the 

emotional toll. In Proceedings of the sixth international workshop on computing education research 

(pp. 77-85). ACM. https://doi.org/10.1145/1839594.1839609

 

Kirschner, P. A., Ayres, P., & Chandler, P. (2011). Contemporary cognitive load theory research: 

The good, the bad and the ugly. Computers in Human Behavior, 27(1), 99-105. 

http://doi.org/10.1016/j.chb.2010.06.025

 

Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does 

not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and 

inquiry-based teaching. Educational Psychologist, 41(2), 75-86.

 

Kohn, T. (2017, March). Variable evaluation: An exploration of novice programmers' understanding 

and common misconceptions. In Proceedings of the 2017 ACM SIGCSE technical symposium on computer 

science education (pp. 345-350).

ACM. https://doi.org/10.1145/3017680.3017724

 

Koo, T. K., & Li, M. Y. (2016). A guideline of selecting and reporting intraclass correlation 

coefficients for reliability research. Journal of Chiropractic Medicine, 15(2), 155-163. 

https://doi.org/10.1016/j.jcm.2016.02.012

 

Kopec, D., Yarmish, G., & Cheung, P. (2007). A description and study of intermediate student 

programmer errors. ACM SIGCSE Bulletin, 39(2), 146-156.

https://doi.org/10.1145/1272848.1272899

 

Kranch, D. A. (2012). Teaching the novice programmer: A study of instructional sequences and 

perception. Education and Information Technologies, 17(3), 291- 313. 

https://doi.org/10.1007/s10639-011-9158-8

 

Kumar, A. N. (2015, June). Automated generation of self-explanation questions in worked examples in 

a model-based tutor. In International Conference on Artificial Intelligence in Education (pp. 

682-685). Springer, Cham. https://doi.org/10.1007/978-3-319-19773-9_91

 

Kunkle, W. M., & Allen, R. B. (2016). The impact of different teaching approaches and languages on 

student learning of introductory programming concepts. ACM Transactions on Computing Education 

(TOCE), 16(1), 1-26. ACM. http://dx.doi.org/10.1145/2785807

 

Kyun, S., Kalyuga, S., & Sweller, J. (2013). The effect of worked examples when learning to write 

essays in English literature. Journal of Experimental Education, 81(3), 385-408. 

http://doi.org/10.1080/00220973.2012.727884

 

Kyun, S., & Lee, H. (2009). The effects of worked examples in computer-based instruction: Focus on 

the presentation format of worked examples and prior knowledge of learners. Asia Pacific Education 

Review, 10(4), 495-503. http://doi.org/10.1007/s12564-009-9044-x

 

Lahtinen, E., Ala-Mutka, K., & Järvinen, H.-M. (2005). A study of the difficulties of novice 

programmers. ACM SIGCSE Bulletin, 37(3), 14-18. http://doi.org/10.1145/1151954.1067453

 

Landers, R. N., & Reddock, C. M. (2017). A meta-analytic investigation of objective learner control 

in web-based instruction. Journal of Business and Psychology, 32(4), 455-478. 

http://doi.org/10.1007/s10869-016-9452-y

 

Lane, H. C., & VanLehn, K. (2005). Teaching the tacit knowledge of programming to novices with 

natural language tutoring. Computer Science Education, 15(3), 183- 201. 

http://doi.org/10.1080/08993400500224286

 

Lee, M. J., & Ko, A. J. (2015, August). Comparing the effectiveness of online learning approaches 

on CS1 learning outcomes. In Proceedings of the eleventh annual international conference on 

international computing education research (pp. 237-246). http://dx.doi.org/10.1145/2787622.2787709

 

Liaw, S. S. (2008). Investigating students’ perceived satisfaction, behavioral intention, and 

effectiveness of e-learning: A case study of the Blackboard system. Computers & Education, 51(2), 

864-873.

https://doi.org/10.1016/j.compedu.2007.09.005

 

Lin, L., Atkinson, R. K., Savenye, W. C., & Nelson, B. C. (2016). Effects of visual cues and 

self-explanation prompts: Empirical evidence in a multimedia environment. Interactive Learning 

Environments, 24(4), 799-813. http://doi.org/10.1080/10494820.2014.924531

 

Linn, M. C., & Clancy, M. J. (1992). The case for case studies of programming problems. 

Communications of the ACM, 35(3), 121-132. https://doi.org/10.1145/131295.131301

 

Lister, R., Simon, B., Thompson, E., Whalley, J. L., & Prasad, C. (2006). Not seeing the forest for 

the trees: novice programmers and the SOLO taxonomy. ACM SIGCSE Bulletin, 38(3), 118-122. 

http://doi.org/10.1145/1140123.1140157

 

Loksa, D., & Ko, A. J. (2016, September). The role of self-regulation in programming problem 

solving process and success. In Proceedings of the 2016 ACM international conference on compuing 

education research (pp. 83-91). ACM. https://doi.org/10.1145/2960310.2960334

 

Lorch, R. F. (1989). Text-signaling devices and their effects on reading and memory processes. 

Educational Psychology Review, 1(3), 209-234. http://doi.org/10.1007/BF01320135

 

Lorch, R., Lemarié, J., & Grant, R. (2011). Signaling hierarchical and sequential organization in 

expository text. Scientific Studies of Reading, 15(3), 267-284. 

http://doi.org/10.1080/10888431003747535

 

Lui, A. K., Cheung, Y. H., & Li, S. C. (2008). Leveraging students' programming laboratory work as 

worked examples. ACM SIGCSE Bulletin, 40(2), 69-73. https://doi.org/10.1145/1383602.1383638

 

Lui, A. K., Kwan, R., Poon, M., & Cheung, Y. H. (2004). Saving weak programming students: applying 

constructivism in a first programming course. ACM SIGCSE Bulletin, 36(2), 72-76. ACM. 

http://dx.doi.org/10.1145/1024338.1024376

 

Luxton-Reilly, A. (2016, July). Learning to program is easy. In Proceedings of the 2016 ACM 

conference on innovation and technology in computer science education (pp. 284-289). 

http://doi.org/10.1145/2899415.2899432

 

Luxton-Reilly, A., Simon, Albluwi, I., Becker, B. A., Giannakos, M., Kumar, A. N., Ott, L., 

Paterson, J., Scott, M. J., Sheard, J., & Szabo, C. (2018, July).

Introductory programming: a systematic literature review. In Proceedings companion of the 23rd 

annual ACM conference on innovation and technology in computer science education (pp. 55-106).

http://dx.doi.org/10.1145/3293881.3295779

 

Malhotra, V. M., & Anand, A. (2019, January). Teaching a university-wide programming laboratory: 

managing a C programming laboratory for a large class with diverse interests. In Proceedings of the 

twenty-first Australasian computing education conference (pp. 1-10). Australian Computer Society, 

Inc. https://doi.org/10.1145/3286960.3286961

 

Maloney, J., Resnick, M., Rusk, N., Silverman, B., & Eastmond, E. (2010). The Scratch programming 

language and environment. ACM Transactions on Computing Education, 10(4), 16:1–16:15. 

http://doi.org/10.1145/1868358.1868363

 

Mandrekar, J. N. (2011). Measures of interrater agreement. Journal of Thoracic Oncology, 6(1), 6-7. 

https://doi.org/10.1097/JTO.0b013e318200f983

 

Margulieux, L. E., & Catrambone, R. (2016). Improving problem solving with subgoal labels in 

expository text and worked examples. Learning and Instruction, 42, 58-71. 

http://doi.org/10.1016/j.learninstruc.2015.12.002

 

Margulieux, L. E., Catrambone, R., & Guzdial, M. (2016). Employing subgoals in computer programming 

education. Computer Science Education, 26(1), 44-67. https://doi.org/10.1080/08993408.2016.1144429

 

Mason, R., Cooper, G., & de Raadt, M. (2012, January). Trends in introductory programming courses 

in Australian universities: languages, environments and pedagogy. In Proceedings of the fourteenth 

Australasian computing education conference (Vol. 123, pp. 33-42). Australian Computer Society, 

Inc..

 

Mathieson, K. (2012). Exploring student perceptions of audiovisual feedback via screencasting in 

online courses. American Journal of Distance

Education, 26(3), 143-156. https://doi.org/10.1080/08923647.2012.689166

 

Mautone, P. D., & Mayer, R. E. (2001). Signaling as a cognitive guide in multimedia learning. 

Journal of Educational Psychology, 93(2), 377-389. http://doi.org/10.1037/0022-0663.93.2.377

 

Mayer, R. E. (2004). Should there be a three-strikes rule against pure discovery learning?. 

American Psychologist, 59(1), 14-19. https://doi.org/10.1037/0003- 066X.59.1.14

 

Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. 

Educational Psychologist, 38(1), 43-52. http://doi.org/10.1207/S15326985EP3801_6

 

McCartney, R., Boustedt, J., Eckerdal, A., Sanders, K., & Zander, C. (2013, August).

Can first-year students program yet? : a study revisited. In Proceedings of the ninth annual 

international ACM conference on international computing

education research (pp. 91-98). http://doi.org/10.1145/2493394.2493412

 

McCracken, M., Almstrum, V., Diaz, D., Guzdial, M., Hagan, D., Kolikant, Y. B.-D., Laxer, C., 

Thomas, L., Utting, I., & Wilusz, T. (2001, December). A multi- national, multi-institutional study 

of assessment of programming skills of first- year CS students. In Working group reports from 

ITiCSE on innovation and technology in computer science education (pp. 125-180). 

https://doi.org/10.1145/572133.572137

 

McLaren, B. M., Adams, D. M., & Mayer, R. E. (2015). Delayed learning effects with erroneous 

examples: A study of learning decimals with a web-based tutor.

International Journal of Artificial Intelligence in Education, 25(4), 520-542. 

http://doi.org/10.1007/s40593-015-0064-x

 

McLaren, B. M., van Gog, T., Ganoe, C., Karabinos, M., & Yaron, D. (2016). The efficiency of worked 

examples compared to erroneous examples, tutored problem solving, and problem solving in 

computer-based learning environments. Computers in Human Behavior, 55, 87-99. 

http://doi.org/10.1016/j.chb.2015.08.038

 

Medeiros, R. P., Ramalho, G. L., & Falcão, T. P. (2019). A systematic literature review on teaching 

and learning introductory programming in higher education. IEEE Transactions on Education, 62(2), 

77-90. https://doi.org/10.1109/TE.2018.2864133

 

Miller, D. (2010). Using a three-step method in a calculus class: Extending the worked example. 

College Teaching, 58(3), 99-104. http://doi.org/10.1080/87567550903521249

 

Miot, H. A. (2016). Agreement analysis in clinical and experimental trials. Jornal Vascular 

Brasileiro, 15(2), 89-92.

 

Moreno, R. (2006a). Learning in high-tech and multimedia environments. Current Directions in 

Psychological Science, 15(2), 63-67. http://doi.org/10.1111/j.0963- 7214.2006.00408.x

 

Moreno, R. (2006b). When worked examples don’t work: Is cognitive load theory at an impasse?. 

Learning and Instruction, 16(2), 170-181. http://doi.org/10.1016/j.learninstruc.2006.02.006

 

Moreno, R. (2010). Cognitive load theory: More food for thought. Instructional Science, 38(2), 

135-141. http://doi.org/10.1007/s11251-009-9122-9

 

Moreno, R., & Mayer, R. E. (2007). Interactive multimodal learning environments.

Educational Psychology Review, 19(3), 309-326. http://doi.org/10.1007/s10648-

007-9047-2

 

Moreno, R., Reisslein, M., & Delgoda, G. M. (2006, October). Toward a fundamental

understanding of worked example instruction: Impact of means-ends practice,

backward/forward fading, and adaptivity. In Proceedings frontiers in education

36th annual conference (pp. 5-10). IEEE. http://doi.org/10.1109/FIE.2006.322285

 

Morrison, B. B., Decker, A., & Margulieux, L. E. (2016, September). Learning loops: A replication 

study illuminates impact of HS courses. In Proceedings of the 2016 ACM conference on international 

computing education research (pp. 221-230). ACM. https://doi.org/10.1145/2960310.2960330

 

Morrison, B. B., Margulieux, L. E., & Guzdial, M. (2015, August). Subgoals, context, and worked 

examples in learning computing problem solving. In Proceedings of the eleventh annual international 

conference on international computing education research (pp. 21-29). ACM. 

http://doi.org/10.1145/2787622.2787733

 

Moura, I. C. (2012, July). Worked-out examples in a computer science introductory module. In 

Proceedings of the world congress on engineering (pp.1082-1085). 

http://www.iaeng.org/publication/WCE2012/WCE2012_pp1082-1085.pdf

 

Moura, I. C. (2013, July). Visualizing the execution of programming worked-out examples with 

Portugol. In Proceedings of the world congress on engineering (pp. 404-408). 

http://www.iaeng.org/publication/WCE2013/WCE2013_pp404-408.pdf

 

Mulder, Y. G., Lazonder, A. W., & De Jong, T. (2014). Using heuristic worked examples to promote 

inquiry-based learning. Learning and Instruction, 29, 56-

64. https://doi.org/10.1016/j.learninstruc.2013.08.001

 

Najar, A. S., Mitrovic, A., & McLaren, B. M. (2014). Adaptive support versus alternating worked 

examples and tutored problems: Which leads to better learning? In V. Dimitrova, T. Kuflik, D. Chin, 

F. Ricci, P. Dolog, G. J. Houben (Eds.), User Modeling, Adaptation, and Personalization: Lecture 

Notes in Computer Science, 8538, 171-182. Springer, Cham. http://doi.org/10.1007/978- 

3-319-08786-3_15

 

Nandigam, D., & Bathula, H. (2013). Competing dichotomies in teaching computer programming to 

beginner-students. American Journal of Educational Research, 1(8), 307-312. 

http://doi.org/10.12691/education-1-8-7

 

Nelson, B. C., Kim, Y., & Slack, K. (2016). Visual signaling in a high-search virtual world-based 

assessment: A SAVE science design study. Technology, Knowledge and Learning, 21(2), 211–224. 

http://doi.org/10.1007/s10758-016-9281-0

 

Newby, P. (2014). Research methods for education (2nd ed.). Routledge.

 

Nugroho, M. A., Setyorini, D., & Novitasari, B. T. (2019). The role of satisfaction on perceived 

value and e-learning usage continuity relationship. Procedia Computer

Science, 161, 82-89. https://doi.org/10.1016/j.procs.2019.11.102

 

Ozcelik, E., Arslan-Ari, I., & Cagiltay, K. (2010). Why does signaling enhance multimedia learning? 

Evidence from eye movements. Computers in Human Behavior, 26(1), 110-117. 

http://doi.org/10.1016/j.chb.2009.09.001

 

Paas, F. G. W. C., Renkl, A., & Sweller, J. (2003). Cognitive load theory and instructional design: 

Recent developments. Educational Psychologist, 38(1), 1-4. http://doi.org/10.1207/S15326985EP3801_1

 

Pallant, J. (2013). SPSS survival manual (5th ed.). McGraw-Hill Education.

 

Parsons, D., Wood, K., & Haden, P. (2015, January). What are we doing when we assess programming?. 

In Proceedings of the 17th Australasian computing education conference (pp. 119-127). Australian 

Computer Society.

 

Peart, D. J., Rumbold, P. L., Keane, K. M., & Allin, L. (2017). Student use and perception of 

technology enhanced learning in a mass lecture knowledge-rich domain first year undergraduate 

module. International Journal of Educational Technology in Higher Education, 14(1), 40. 

https://doi.org/10.1186/s41239-017- 0078-6

 

Price, T. W., & Barnes, T. (2015, August). Comparing textual and block interfaces in a novice 

programming environment. In Proceedings of the eleventh annual international conference on 

international computing education research (pp. 91- 99). http://doi.org/10.1145/2787622.2787712

 

Proulx, V. K. (2000). Programming patterns and design patterns in the introductory computer science 

course. ACM SIGCSE Bulletin, 32(1), 80-84. http://doi.org/10.1145/331795.331819

 

Prunuske, A. J., Henn, L., Brearley, A. M., & Prunuske, J. (2016). A randomized crossover design to 

assess learning impact and student preference for active and passive online learning modules. 

Medical Science Educator, 26(1), 135-141. https://doi.org/10.1007/s40670-015-0224-5

 

Qian, Y., & Lehman, J. (2017). Students’ misconceptions and other difficulties in introductory 

programming: A literature review. ACM Transactions on Computing Education, 18(1), 1-24. 

https://doi.org/10.1145/3077618

 

Quilici, J. L., & Mayer, R. E. (2002). Teaching students to recognize structural similarities 

between statistics word problems. Applied Cognitive Psychology, 16(3), 325-342. 

http://doi.org/10.1002/acp.796

 

Reeves, T. C., & Oh, E. G. (2017). The goals and methods of educational technology research over a 

quarter century (1989-2014). Educational Technology Research

and Development, 65(2), 325-339. http://doi.org/10.1007/s11423-016-9474-1

 

Renkl, A. (2014). Toward an instructionally oriented theory of example-based learning. Cognitive 

Science, 38(1), 1-37. https://doi.org/10.1111/cogs.12086

 

Renkl, A. (2017). Learning from worked-examples in mathematics: students relate procedures to 

principles. ZDM Mathematics Education, 49(4), 571-584. https://doi.org/10.1007/s11858-017-0859-3

 

Renkl, A., & Atkinson, R. K. (2002). Learning from examples: Fostering self- explanations in 

computer-based learning environments. Interactive Learning Environments, 10(2), 105-119. 

http://doi.org/10.1076/ilee.10.2.105.7441

 

Renkl, A., & Atkinson, R. K. (2003). Structuring the transition from example study to problem 

solving in cognitive skill acquisition: A cognitive load perspective.

Educational Psychologist, 38(1), 15-22. http://doi.org/10.1207/S15326985EP3801_3

 

Renkl, A., & Atkinson, R. K. (2007). Interactive learning environments: Contemporary issues and 

trends. An introduction to the special issue. Educational Psychology Review, 19(3), 235-238. 

http://doi.org/10.1007/s10648-

007-9052-5

 

Renkl, A., Atkinson, R. K., Maier, U. H., & Staley, R. (2002). From example study to problem 

solving: Smooth transitions help learning. The Journal of Experimental Education, 70(4), 293-315. 

http://doi.org/10.1080/00220970209599510

 

Richey, J. E., & Nokes-Malach, T. J. (2013). How much is too much? Learning and motivation effects 

of adding instructional explanations to worked examples.

Learning and Instruction, 25, 104-124. http://doi.org/10.1016/j.learninstruc.2012.11.006

 

Rist, R. S. (1996). Teaching Eiffel as a first language. Journal of Object-Oriented Programming, 

9(1), 30-41.

 

Robins, A. (2010). Learning edge momentum: A new account of outcomes in CS1.

Computer Science Education, 20(1), 37-71. http://dx.doi.org/10.1080/08993401003612167

 

Robins, A., Rountree, J., & Rountree, N. (2003). Learning and teaching programming: A review and 

discussion. Computer Science Education, 13(2), 137-172. http://doi.org/10.1076/csed.13.2.137.14200

 

Sanford, J. P., Tietz, A., Farooq, S., Guyer, S., & Shapiro, R. B. (2014, March).

Metaphors we teach by. In Proceedings of the 45th ACM technical symposium on computer science 

education (pp. 585-590).

http://dx.doi.org/10.1145/2538862.2538945

 

Schindler, M., & Lilienthal, A. J. (2019). Domain-specific interpretation of eye tracking data: 

towards a refined use of the eye-mind hypothesis for the field of geometry. Educational Studies in 

Mathematics, 101(1), 123-139. http://dx.doi.org/10.1007/s10649-019-9878-z

 

Schoenfeld, A. H. (1980). Teaching problem-solving skills. The American Mathematical Monthly, 

87(10), 794-805. http://dx.doi.org/10.1080/00029890.1980.11995155

 

Schoenfeld, A. H. (1992). Learning to think mathematically: Problem solving, metacognition, and 

sense making in mathematics. In D. A. Grouws (Ed.), Handbook of research on mathematics teaching 

and learning (pp. 334–370). Macmillan.

 

Schulte, C. (2008, September). Block Model: an educational model of program comprehension as a tool 

for a scholarly approach to teaching. In Proceedings of the fourth international workshop on 

computing education research (pp. 149- 160). http://dx.doi.org/10.1145/1404520.1404535

 

Schulte, C., Clear, T., Taherkhani, A., Busjahn, T., & Paterson, J. H. (2010). An introduction to 

program comprehension for computer science educators. In Proceedings of the 2010 ITiCSE working 

group reports (pp. 65-86). http://dx.doi.org/10.1145/1971681.1971687

 

Schwonke, R., Renkl, A., Krieg, C., Wittwer, J., Aleven, V., & Salden, R. (2009). The 

worked-example effect: Not an artefact of lousy control conditions. Computers in Human Behavior, 

25(2), 258-266. http://doi.org/10.1016/j.chb.2008.12.011

 

Selby, C. C. (2015, November). Relationships: computational thinking, pedagogy of programming, and 

Bloom's Taxonomy. In Proceedings of the workshop in primary and secondary computing education (pp. 

80-87). http://dx.doi.org/10.1145/2818314.2818315

 

Seppälä, O., Ihantola, P., Isohanni, E., Sorva, J., & Vihavainen, A. (2015, November).

Do we know how difficult the rainfall problem is?. In Proceedings of the 15th Koli calling 

conference on computing education research (pp. 87-96). https://doi.org/10.1145/2828959.2828963

 

Skudder, B., & Luxton-Reilly, A. (2014, January). Worked examples in computer science. In 

Proceedings of the sixteenth Australasian computing education conference (Vol. 148, pp. 59-64). 

Australian Computer Society, Inc..

 

Smith, A. R., Cavanaugh, C., & Moore, W. A. (2011). Instructional multimedia: An investigation of 

student and instructor attitudes and student study

behavior. BMC Medical Education, 11(1), 38-49.

http://dx.doi.org/10.1186/1472-6920-11-38

 

Soloway, E. (1986). Learning to program = learning to construct mechanisms and

explanations. Communications of the ACM, 29(9), 850-858. http://dx.doi.org/10.1145/6592.6594

Soloway, E., & Ehrlich, K. (1984). Empirical studies of programming knowledge.

IEEE Transactions on Software Engineering, 10(5), 595-609. http://doi.org/10.1109/TSE.1984.5010283

 

Sorva, J., Karavirta, V., & Malmi, L. (2013). A review of generic program visualization systems for 

introductory programming education. ACM Transactions on Computing Education (TOCE), 13(4), 1-64. 

http://dx.doi.org/10.1145/2490822

 

Sorva, J., & Seppälä, O. (2014, November). Research-based design of the first weeks of CS1. In 

Proceedings of the 14th Koli calling international conference on computing education research (pp. 

71-80). http://dx.doi.org/10.1145/2674683.2674690

 

Strauss, A., & Corbin, J. (1994). Grounded theory methodology: An overview. In

Handbook of qualitative research (pp. 273-285). Sage Publications.

 

Sweller, J., & Cooper, G. A. (1985). The use of worked examples as a substitute for problem solving 

in learning algebra. Cognition and Instruction, 2(1), 59-89. 

http://doi.org/10.1207/s1532690xci0201_3

 

Sweller, J., van Merriënboer, J. J., & Paas, F. (2019). Cognitive architecture and instructional 

design: 20 years later. Educational Psychology Review, 31(2), 261- 292. 

http://dx.doi.org/10.1007/s10648-019-09465-5

 

Tan, P. H., Ting, C. Y., & Ling, S. W. (2009, November). Learning difficulties in programming 

courses: undergraduates' perspective and perception. In 2009 International Conference on Computer 

Technology and Development (Vol. 1, pp. 42-46). IEEE. http://dx.doi.org/10.1109/ICCTD.2009.188

 

Teague, D., & Lister, R. (2014, January). Longitudinal think aloud study of a novice programmer. In 

Proceedings of the sixteenth Australasian computing education conference (Vol. 148, pp. 41-50). 

Australian Computer Society, Inc..

 

Tepgeç, M., & Çevik, Y. D. (2018). Comparison of three instructional strategies in teaching 

programming: restudying material, testing and worked

example. Journal of Learning and Teaching in Digital Age, 3(2), 42-50.

 

Trevethan, R. (2017). Intraclass correlation coefficients: clearing the air, extending some 

cautions, and making some requests. Health Services and Outcomes Research Methodology, 17(2), 

127-143. http://dx.doi.org/10.1007/s10742-016-

0156-6

 

van    og, T. (2011). Effects of identical example-problem and problem-example pairs

on learning. Computers & Education, 57(2), 1775-1779. http://doi.org/10.1016/j.compedu.2011.03.019

 

van Gog, T., Kester, L., & Paas, F. G. W. C. (2011). Effects of worked examples, example-problem, 

and problem-example pairs on novices’ learning.

Contemporary Educational Psychology, 36(3), 212-218. http://doi.org/10.1016/j.cedpsych.2010.10.004

 

van Gog, T., Paas, F. G. W. C., & van Merriënboer, J. J. G. (2006). Effects of process-oriented 

worked examples on troubleshooting transfer performance. Learning and Instruction, 16(2), 154-164. 

http://doi.org/10.1016/j.learninstruc.2006.02.003

 

van Merriënboer, J. J. G. (2013). Perspectives on problem solving and instruction.

Computers & Education, 64, 153-160. http://doi.org/10.1016/j.compedu.2012.11.025

 

van Merriënboer, J. J. G., & Sweller, J. (2005). Cognitive load theory and complex learning: Recent 

developments and future directions. Educational Psychology Review, 17(2), 147-177. 

http://doi.org/10.1007/s10648-005-3951-0

 

Vieira, C., Yan, J., & Magana, A. J. (2015). Exploring design characteristics of worked examples to 

support programming and algorithm design. Journal of Computational Science Education, 6(1), 2-15. 

https://doi.org/10.22369/issn.2153-4136/6/1/1

 

Vihavainen, A., Miller, C. S., & Settle, A. (2015, February). Benefits of self- explanation in 

introductory programming. In Proceedings of the 46th ACM technical symposium on computer science 

education (pp. 284-289). http://dx.doi.org/10.1145/2676723.2677260

 

Ward, M., & Sweller, J. (1990). Structuring effective worked examples. Cognition and Instruction, 

7(1), 1-39. http://doi.org/10.1207/s1532690xci0701_1

 

Watson, C., & Li, F. (2014, June). Failure rates in introductory programming revisited. In 

Proceedings of the 2014 conference on innovation & technology in computer science education (pp. 

39-44). http://doi.org/10.1145/2591708.2591749

 

Whalley, J., & Kasto, N. (2014, January). How difficult are novice code writing tasks? A software 

metrics approach. In Proceedings of the sixteenth Australasian computing education conference (Vol. 

148, pp. 105-112).

 

Whalley, J. L., Lister, R., Thompson, E., Clear, T., Robbins, P., Ajith Kumar, P. K., &

Prasad, C. (2006, December). An Australasian study of reading and comprehension skills in novice programmers, using the Bloom and SOLO taxonomies. In Proceedings of 

the 8th Australasian conference on computing education (Vol. 52, pp. 243–252). Australian Computer 

Society.

 

Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33- 35. 

http://doi.org/10.1145/1118178.1118215

 

Winslow, L. E. (1996). Programming pedagogy - a psychological overview. ACM SIGCSE Bulletin, 28(3), 

17-22. http://doi.org/10.1145/234867.234872

 

Wismath, S., Orr, D., & MacKay, B. (2015). Threshold concepts in the development of problem-solving 

skills. Teaching & Learning Inquiry The ISSOTL Journal, 3(1), 63-73. 

http://dx.doi.org/10.20343/teachlearninqu.3.1.63

 

Yan, J., & Lavigne, N. C. (2014). Promoting college students’ problem understanding using 

schema-emphasizing worked examples. The Journal of Experimental Education, 82(1), 74-102. 

http://dx.doi.org/10.1080/00220973.2012.745466

 

Zhi, R., Price, T.W., Marwan, S., Milliken, A., Barnes, T. & Chi, M. (2019, February). Exploring 

the impact of worked examples in a novice programming environment. In Proceedings of the 50th ACM 

technical symposium on computer science education. (pp. 98-104). ACM. 

http://dx.doi.org/10.1145/3287324.3287385

 

Zientek, L., Nimon, K., & Hammack-Brown, B. (2016). Analyzing data from a pretest-posttest control 

group design. European Journal of Training and

Development, 40(8/9), 638-659. http://dx.doi.org/10.1108/EJTD-08-2015-0066

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 


This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials.
You may use the digitized material for private study, scholarship, or research.

Back to previous page

Installed and configured by Bahagian Automasi, Perpustakaan Tuanku Bainun, Universiti Pendidikan Sultan Idris
If you have enquiries with this repository, kindly contact us at pustakasys@upsi.edu.my or Whatsapp +60163630263 (Office hours only)