Adaptation of the Growth Mindset Scale into Indonesian Language and Culture
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This study aims to adapt a growth mindset measurement tool into Indonesian language and culture and test its validity and reliability. The adaptation process followed the International Test Commission guidelines. Respondents were 310 people aged 18–25 years old spread across various regions of Indonesia. The results of Confirmatory Factor Analysis (CFA) showed a good fit of the measurement model (RMSEA = 0.062; CFI = 0.948; TLI = 0.928; SRMR = 0.040). Eight items in the scale were declared valid (Aiken's V > 0.72) and reliable with a McDonald's Omega value of 0.774. These findings indicate that the adapted measurement tool is suitable for measuring growth mindset in young adults in Indonesia. This instrument can be used in assessments, research, and psychological interventions aimed at increasing resilience and motivation in facing the challenges of early adult development.
Aghapour, Z. (2023). Task offloading and resource allocation algorithm based on deep reinforcement learning for distributed AI execution tasks in IoT edge computing environments. Computer Networks, 223(Query date: 2025-03-15 16:54:26). https://doi.org/10.1016/j.comnet.2023.109577
Alam, A. (2022). Social Robots in Education for Long-Term Human-Robot Interaction: Socially Supportive Behaviour of Robotic Tutor for Creating Robo-Tangible Learning Environment in a Guided Discovery Learning Interaction. ECS Transactions, 107(1), 12389–12403. https://doi.org/10.1149/10701.12389ecst
Alemayehu, L. (2023). The influence of motivation on learning engagement: The mediating role of learning self-efficacy and self-monitoring in online learning environments. Interactive Learning Environments, 31(7), 4605–4618. https://doi.org/10.1080/10494820.2021.1977962
Alvarez, R. P. (2022). Tools Designed to Support Self-Regulated Learning in Online Learning Environments: A Systematic Review. IEEE Transactions on Learning Technologies, 15(4), 508–522. https://doi.org/10.1109/TLT.2022.3193271
Barrett, A. (2023). Technology acceptance model and multi-user virtual reality learning environments for Chinese language education. Interactive Learning Environments, 31(3), 1665–1682. https://doi.org/10.1080/10494820.2020.1855209
Barrot, J. S. (2022). Social media as a language learning environment: A systematic review of the literature (2008-2019). Computer Assisted Language Learning, 35(9), 2534–2562. https://doi.org/10.1080/09588221.2021.1883673
Bateman, A. (2023). UniProt: The Universal Protein Knowledgebase in 2023. Nucleic Acids Research, 51(Query date: 2025-07-17 20:15:23). https://doi.org/10.1093/nar/gkac1052
Cayubit, R. F. O. (2022). Why learning environment matters? An analysis on how the learning environment influences the academic motivation, learning strategies and engagement of college students. Learning Environments Research, 25(2), 581–599. https://doi.org/10.1007/s10984-021-09382-x
Christensen, Larry. B. (2007). Experimental methodology. Pearson.
Deng, X. (2022). Towards optimal HVAC control in non-stationary building environments combining active change detection and deep reinforcement learning. Building and Environment, 211(Query date: 2025-03-15 16:54:26). https://doi.org/10.1016/j.buildenv.2021.108680
Feigin, V. L. (2022). World Stroke Organization (WSO): Global Stroke Fact Sheet 2022. International Journal of Stroke, 17(1), 18–29. https://doi.org/10.1177/17474930211065917
Guo, L. (2022). Using metacognitive prompts to enhance self-regulated learning and learning outcomes: A meta-analysis of experimental studies in computer-based learning environments. Journal of Computer Assisted Learning, 38(3), 811–832. https://doi.org/10.1111/jcal.12650
Hatamizadeh, A. (2022). UNETR: Transformers for 3D Medical Image Segmentation. Proceedings 2022 IEEE Cvf Winter Conference on Applications of Computer Vision Wacv 2022, Query date: 2025-07-17 20:15:23, 1748–1758. https://doi.org/10.1109/WACV51458.2022.00181
Iqbal, J. (2022). The Impacts of Emotional Intelligence on Students’ Study Habits in Blended Learning Environments: The Mediating Role of Cognitive Engagement during COVID-19. Behavioral Sciences, 12(1). https://doi.org/10.3390/BS12010014
Liu, H. (2022). Transsee: Enabling environment-independent mmwave sensing based gesture recognition via transfer learning. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 6(1). https://doi.org/10.1145/3517231
Liu, H. (2023). Visual Instruction Tuning. Advances in Neural Information Processing Systems, 36(Query date: 2025-07-17 20:15:23). https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85191171970&origin=inward
Liu, J. (2022). Wnt/?-catenin signalling: Function, biological mechanisms, and therapeutic opportunities. Signal Transduction and Targeted Therapy, 7(1). https://doi.org/10.1038/s41392-021-00762-6
Liu, T. (2022). Uncovering students’ problem-solving processes in game-based learning environments. Computers and Education, 182(Query date: 2025-03-15 16:54:26). https://doi.org/10.1016/j.compedu.2022.104462
Lu, G. (2022). What influences student situational engagement in smart classrooms: Perception of the learning environment and students’ motivation. British Journal of Educational Technology, 53(6), 1665–1687. https://doi.org/10.1111/bjet.13204
Miao, J. (2022). Teacher–Student Interaction, Student–Student Interaction and Social Presence: Their Impacts on Learning Engagement in Online Learning Environments. Journal of Genetic Psychology, 183(6), 514–526. https://doi.org/10.1080/00221325.2022.2094211
Qiao, Y. (2023). Survey of Deep Learning for Autonomous Surface Vehicles in Marine Environments. IEEE Transactions on Intelligent Transportation Systems, 24(4), 3678–3701. https://doi.org/10.1109/TITS.2023.3235911
Quadir, B. (2022). The effects of interaction types on learning outcomes in a blog-based interactive learning environment. Interactive Learning Environments, 30(2), 293–306. https://doi.org/10.1080/10494820.2019.1652835
Rusticus, S. A. (2023). What are the key elements of a positive learning environment? Perspectives from students and faculty. Learning Environments Research, 26(1), 161–175. https://doi.org/10.1007/s10984-022-09410-4
Sauer, P. C. (2022). Supply chains’ sustainability trajectories and resilience: A learning perspective in turbulent environments. International Journal of Operations and Production Management, 42(8), 1109–1145. https://doi.org/10.1108/IJOPM-12-2021-0759
Schneider, S. (2022). The Cognitive-Affective-Social Theory of Learning in digital Environments (CASTLE). Educational Psychology Review, 34(1). https://doi.org/10.1007/s10648-021-09626-5
Shang, Y. (2023). Using lightweight deep learning algorithm for real-time detection of apple flowers in natural environments. Computers and Electronics in Agriculture, 207(Query date: 2025-03-15 16:54:26). https://doi.org/10.1016/j.compag.2023.107765
Shi, A. (2022). The effect of game–based immersive virtual reality learning environment on learning outcomes: Designing an intrinsic integrated educational game for pre–class learning. Interactive Learning Environments, 30(4), 721–734. https://doi.org/10.1080/10494820.2019.1681467
Sun, C. (2022). The relationship between collaborative problem solving behaviors and solution outcomes in a game-based learning environment. Computers in Human Behavior, 128(Query date: 2025-03-15 16:54:26). https://doi.org/10.1016/j.chb.2021.107120
Talukdar, V. (2022). Suspicious Activity Detection and Classification in IoT Environment Using Machine Learning Approach. PDGC 2022 - 2022 7th International Conference on Parallel, Distributed and Grid Computing, Query date: 2025-03-15 16:54:26, 531–535. https://doi.org/10.1109/PDGC56933.2022.10053312
Traue, A. (2022). Toward a Reinforcement Learning Environment Toolbox for Intelligent Electric Motor Control. IEEE Transactions on Neural Networks and Learning Systems, 33(3), 919–928. https://doi.org/10.1109/TNNLS.2020.3029573
Turk, M. (2022). Teaching and social presences supporting basic needs satisfaction in online learning environments: How can presences and basic needs happily meet online? Computers and Education, 180(Query date: 2025-03-15 16:54:26). https://doi.org/10.1016/j.compedu.2022.104432
Vergara-Rodríguez, D. (2022). Variables Influencing Professors’ Adaptation to Digital Learning Environments during the COVID-19 Pandemic. International Journal of Environmental Research and Public Health, 19(6). https://doi.org/10.3390/ijerph19063732
Wang, C. Y. (2023). YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2023(Query date: 2025-07-17 20:15:23), 7464–7475. https://doi.org/10.1109/CVPR52729.2023.00721
Wang, J. (2022). Teacher beliefs, classroom process quality, and student engagement in the smart classroom learning environment: A multilevel analysis. Computers and Education, 183(Query date: 2025-03-15 16:54:26). https://doi.org/10.1016/j.compedu.2022.104501
Wang, X. (2022). Solving job scheduling problems in a resource preemption environment with multi-agent reinforcement learning. Robotics and Computer-Integrated Manufacturing, 77(Query date: 2025-03-15 16:54:26). https://doi.org/10.1016/j.rcim.2022.102324
Wannapiroon, N. (2022). Thai undergraduate science, technology, engineering, arts, and math (STEAM) creative thinking and innovation skill development: A conceptual model using a digital virtual classroom learning environment. Education and Information Technologies, 27(4), 5689–5716. https://doi.org/10.1007/s10639-021-10849-w
Weerakoon, K. (2022). TERP: Reliable Planning in Uneven Outdoor Environments using Deep Reinforcement Learning. Proceedings - IEEE International Conference on Robotics and Automation, Query date: 2025-03-15 16:54:26, 9447–9453. https://doi.org/10.1109/ICRA46639.2022.9812238
Xu, J. (2022). UAV-assisted task offloading for IoT in smart buildings and environment via deep reinforcement learning. Building and Environment, 222(Query date: 2025-03-15 16:54:26). https://doi.org/10.1016/j.buildenv.2022.109218
Xu, Z. (2023). Synthesizing research evidence on self-regulated learning and academic achievement in online and blended learning environments: A scoping review. Educational Research Review, 39(Query date: 2025-03-15 16:54:26). https://doi.org/10.1016/j.edurev.2023.100510
Yu, Z. (2022). Sustaining Student Roles, Digital Literacy, Learning Achievements, and Motivation in Online Learning Environments during the COVID-19 Pandemic. Sustainability (Switzerland), 14(8). https://doi.org/10.3390/su14084388
Zauskova, A. (2022). Visual Imagery and Geospatial Mapping Tools, Virtual Simulation Algorithms, and Deep Learning-based Sensing Technologies in the Metaverse Interactive Environment. Review of Contemporary Philosophy, 21(Query date: 2025-03-15 16:54:26), 122–137. https://doi.org/10.22381/RCP2120228
Zhang, J. (2022). The clinical learning environment, supervision and future intention to work as a nurse in nursing students: A cross-sectional and descriptive study. BMC Medical Education, 22(1). https://doi.org/10.1186/s12909-022-03609-y
Zidi, S. (2023). Theft detection dataset for benchmarking and machine learning based classification in a smart grid environment. Journal of King Saud University - Computer and Information Sciences, 35(1), 13–25. https://doi.org/10.1016/j.jksuci.2022.05.007
