Enhancing Algorithmic Thinking through Computational Tools: A Study on High School Computing Education
Abstract
The importance of algorithmic thinking in modern education has grown significantly, particularly in the context of computing education in high schools. As computational tools become more accessible, their role in enhancing students' problem-solving abilities has gained considerable attention. This study investigates how computational tools can improve algorithmic thinking among high school students and their overall engagement with computing subjects. The primary objective is to assess the impact of integrating computational tools, such as programming environments and visual coding platforms, on students' development of algorithmic skills. The study adopts a mixed-methods approach, combining quantitative data from pre-and post-tests measuring algorithmic thinking skills, and qualitative data through interviews and classroom observations. A total of 150 high school students from various educational backgrounds participated in the study over one semester. The results indicate a significant improvement in students’ algorithmic thinking abilities after exposure to computational tools, particularly in areas such as problem decomposition, abstraction, and logical reasoning. Additionally, students reported higher levels of motivation and interest in computing subjects. In conclusion, the integration of computational tools in high school computing education not only enhances algorithmic thinking but also fosters greater student engagement. These findings highlight the potential of using technology to bridge the gap between theoretical concepts and practical application in computing education. Further research is encouraged to explore long-term effects and the scalability of these methods across diverse educational settings.
Full text article
References
Aguirre Velasco, A., Cruz, I. S. S., Billings, J., Jimenez, M., & Rowe, S. (2020). What are the barriers, facilitators and interventions targeting help-seeking behaviours for common mental health problems in adolescents? A systematic review. BMC Psychiatry, 20(1), 293. https://doi.org/10.1186/s12888-020-02659-0
Al-Balas, M., Al-Balas, H. I., Jaber, H. M., Obeidat, K., Al-Balas, H., Aborajooh, E. A., Al-Taher, R., & Al-Balas, B. (2020). Distance learning in clinical medical education amid COVID-19 pandemic in Jordan: Current situation, challenges, and perspectives. BMC Medical Education, 20(1), 341. https://doi.org/10.1186/s12909-020-02257-4
Alzamzami, F., Hoda, M., & El Saddik, A. (2020). Light Gradient Boosting Machine for General Sentiment Classification on Short Texts: A Comparative Evaluation. IEEE Access, 8, 101840–101858. https://doi.org/10.1109/ACCESS.2020.2997330
AlZu’bi, S., Hawashin, B., Mujahed, M., Jararweh, Y., & Gupta, B. B. (2019). An efficient employment of internet of multimedia things in smart and future agriculture. Multimedia Tools and Applications, 78(20), 29581–29605. https://doi.org/10.1007/s11042-019-7367-0
Arici, F., Yildirim, P., Caliklar, ?., & Yilmaz, R. M. (2019). Research trends in the use of augmented reality in science education: Content and bibliometric mapping analysis. Computers & Education, 142, 103647. https://doi.org/10.1016/j.compedu.2019.103647
Audebert, N., Le Saux, B., & Lefevre, S. (2019). Deep Learning for Classification of Hyperspectral Data: A Comparative Review. IEEE Geoscience and Remote Sensing Magazine, 7(2), 159–173. https://doi.org/10.1109/MGRS.2019.2912563
Barakabitze, A. A., Ahmad, A., Mijumbi, R., & Hines, A. (2020). 5G network slicing using SDN and NFV: A survey of taxonomy, architectures and future challenges. Computer Networks, 167, 106984. https://doi.org/10.1016/j.comnet.2019.106984
Bharti, R., Khamparia, A., Shabaz, M., Dhiman, G., Pande, S., & Singh, P. (2021). Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning. Computational Intelligence and Neuroscience, 2021(1), 8387680. https://doi.org/10.1155/2021/8387680
Bolón-Canedo, V., & Remeseiro, B. (2020). Feature selection in image analysis: A survey. Artificial Intelligence Review, 53(4), 2905–2931. https://doi.org/10.1007/s10462-019-09750-3
Cambra Baseca, C., Sendra, S., Lloret, J., & Tomas, J. (2019). A Smart Decision System for Digital Farming. Agronomy, 9(5), 216. https://doi.org/10.3390/agronomy9050216
Choe, R. C., Scuric, Z., Eshkol, E., Cruser, S., Arndt, A., Cox, R., Toma, S. P., Shapiro, C., Levis-Fitzgerald, M., Barnes, G., & Crosbie, R. H. (2019). Student Satisfaction and Learning Outcomes in Asynchronous Online Lecture Videos. CBE—Life Sciences Education, 18(4), ar55. https://doi.org/10.1187/cbe.18-08-0171
Costa-Sánchez, C., & López-García, X. (2020). Comunicación y crisis del coronavirus en España. Primeras lecciones. El Profesional de la Información, 29(3). https://doi.org/10.3145/epi.2020.may.04
Cozzolino, D., & Verdoliva, L. (2020). Noiseprint: A CNN-Based Camera Model Fingerprint. IEEE Transactions on Information Forensics and Security, 15, 144–159. https://doi.org/10.1109/TIFS.2019.2916364
Erkan, U., Toktas, A., & Lai, Q. (2023). 2D hyperchaotic system based on Schaffer function for image encryption. Expert Systems with Applications, 213, 119076. https://doi.org/10.1016/j.eswa.2022.119076
Fidan, M., & Tuncel, M. (2019). Integrating augmented reality into problem based learning: The effects on learning achievement and attitude in physics education. Computers & Education, 142, 103635. https://doi.org/10.1016/j.compedu.2019.103635
Georgiou, T., Liu, Y., Chen, W., & Lew, M. (2020). A survey of traditional and deep learning-based feature descriptors for high dimensional data in computer vision. International Journal of Multimedia Information Retrieval, 9(3), 135–170. https://doi.org/10.1007/s13735-019-00183-w
Gyawali, S., Xu, S., Qian, Y., & Hu, R. Q. (2021). Challenges and Solutions for Cellular Based V2X Communications. IEEE Communications Surveys & Tutorials, 23(1), 222–255. https://doi.org/10.1109/COMST.2020.3029723
Johnson, J., Douze, M., & Jegou, H. (2021). Billion-Scale Similarity Search with GPUs. IEEE Transactions on Big Data, 7(3), 535–547. https://doi.org/10.1109/TBDATA.2019.2921572
Katoch, S., Chauhan, S. S., & Kumar, V. (2021). A review on genetic algorithm: Past, present, and future. Multimedia Tools and Applications, 80(5), 8091–8126. https://doi.org/10.1007/s11042-020-10139-6
López-Meneses, E., Sirignano, F. M., Vázquez-Cano, E., & Ramírez-Hurtado, J. M. (2020). University students’ digital competence in three areas of the DigCom 2.1 model: A comparative study at three European universities. Australasian Journal of Educational Technology, 69–88. https://doi.org/10.14742/ajet.5583
Luo, H., Jiang, W., Gu, Y., Liu, F., Liao, X., Lai, S., & Gu, J. (2020). A Strong Baseline and Batch Normalization Neck for Deep Person Re-Identification. IEEE Transactions on Multimedia, 22(10), 2597–2609. https://doi.org/10.1109/TMM.2019.2958756
Makransky, G., Borre?Gude, S., & Mayer, R. E. (2019). Motivational and cognitive benefits of training in immersive virtual reality based on multiple assessments. Journal of Computer Assisted Learning, 35(6), 691–707. https://doi.org/10.1111/jcal.12375
Makransky, G., Terkildsen, T. S., & Mayer, R. E. (2019). Adding immersive virtual reality to a science lab simulation causes more presence but less learning. Learning and Instruction, 60, 225–236. https://doi.org/10.1016/j.learninstruc.2017.12.007
Mayer, O., & Stamm, M. C. (2020). Forensic Similarity for Digital Images. IEEE Transactions on Information Forensics and Security, 15, 1331–1346. https://doi.org/10.1109/TIFS.2019.2924552
Meyer, O. A., Omdahl, M. K., & Makransky, G. (2019). Investigating the effect of pre-training when learning through immersive virtual reality and video: A media and methods experiment. Computers & Education, 140, 103603. https://doi.org/10.1016/j.compedu.2019.103603
Mohanarathinam, A., Kamalraj, S., Prasanna Venkatesan, G. K. D., Ravi, R. V., & Manikandababu, C. S. (2020). Digital watermarking techniques for image security: A review. Journal of Ambient Intelligence and Humanized Computing, 11(8), 3221–3229. https://doi.org/10.1007/s12652-019-01500-1
Pang, X., Zhou, Y., Wang, P., Lin, W., & Chang, V. (2020). An innovative neural network approach for stock market prediction. The Journal of Supercomputing, 76(3), 2098–2118. https://doi.org/10.1007/s11227-017-2228-y
Rahman, Md. A., Rashid, Md. M., Hossain, M. S., Hassanain, E., Alhamid, M. F., & Guizani, M. (2019). Blockchain and IoT-Based Cognitive Edge Framework for Sharing Economy Services in a Smart City. IEEE Access, 7, 18611–18621. https://doi.org/10.1109/ACCESS.2019.2896065
Schwarz, S., Preda, M., Baroncini, V., Budagavi, M., Cesar, P., Chou, P. A., Cohen, R. A., Krivokuca, M., Lasserre, S., Li, Z., Llach, J., Mammou, K., Mekuria, R., Nakagami, O., Siahaan, E., Tabatabai, A., Tourapis, A. M., & Zakharchenko, V. (2019). Emerging MPEG Standards for Point Cloud Compression. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 9(1), 133–148. https://doi.org/10.1109/JETCAS.2018.2885981
Verdoliva, L. (2020). Media Forensics and DeepFakes: An Overview. IEEE Journal of Selected Topics in Signal Processing, 14(5), 910–932. https://doi.org/10.1109/JSTSP.2020.3002101
Wang, Z., Ho, S.-B., & Cambria, E. (2020). A review of emotion sensing: Categorization models and algorithms. Multimedia Tools and Applications, 79(47–48), 35553–35582. https://doi.org/10.1007/s11042-019-08328-z
Williams, C., & Beam, S. (2019). Technology and writing: Review of research. Computers & Education, 128, 227–242. https://doi.org/10.1016/j.compedu.2018.09.024
Yang, M., Zhao, W., Xu, W., Feng, Y., Zhao, Z., Chen, X., & Lei, K. (2019). Multitask Learning for Cross-Domain Image Captioning. IEEE Transactions on Multimedia, 21(4), 1047–1061. https://doi.org/10.1109/TMM.2018.2869276
Yao, G., Lei, T., & Zhong, J. (2019). A review of Convolutional-Neural-Network-based action recognition. Pattern Recognition Letters, 118, 14–22. https://doi.org/10.1016/j.patrec.2018.05.018
Zhang, J., Peng, Y., & Yuan, M. (2020). SCH-GAN: Semi-Supervised Cross-Modal Hashing by Generative Adversarial Network. IEEE Transactions on Cybernetics, 50(2), 489–502. https://doi.org/10.1109/TCYB.2018.2868826
Authors
Copyright (c) 2024 Rahmawati Rahmawati, Omar Khan, M Syahputra, Amalia Hanifa, Ediaman Sitepu

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.