Abe, E. N., & Chikoko, V. (2020). Exploring the factors that influence the career decision of STEM students at a university in South Africa. International Journal of STEM Education, 7(1), 60. https://doi.org/10.1186/s40594-020-00256-x
Adukaite, A., van Zyl, I., Er, Ş, & Cantoni, L. (2017). Teacher perceptions on the use of digital gamified learning in tourism education: The case of South African secondary schools. Computers & Education, 111, 172–190. https://doi.org/10.1016/j.compedu.2017.04.008
Apedoe, X. S., Reynolds, B., Ellefson, M. R., & Schunn, C. D. (2008). Bringing Engineering Design into High School Science Classrooms: The Heating/Cooling Unit. Journal of Science Education and Technology, 17(5), 454–465. https://doi.org/10.1007/s10956-008-9114-6.
Ariyanto, S., Munoto, M., & Muhaji, M. (2019). Development of Psychomotor Domain Assessment Instrument on Brake System Competence in SMKN 1 Jetis Mojokerto. International Journal for Educational and Vocational Studies. https://doi.org/10.29103/ijevs.v1i6.1648.
Badri, M., Alnuaimi, A., Mohaidat, J., Al Rashedi, A., Yang, G., & Al Mazroui, K. (2016). My science class and expected career choices—a structural equation model of determinants involving Abu Dhabi high school students. International Journal of STEM Education, 3(1), 12. https://doi.org/10.1186/s40594-016-0045-0.
Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. https://doi.org/10.1037/0022-35188.8.131.523.
Barrett, A., Pack, A., Guo, Y., & Wang, N. (2020). Technology acceptance model and multi-user virtual reality learning environments for Chinese language education. Interactive Learning Environments. https://doi.org/10.1080/10494820.2020.1855209
Barrett, A. J., Pack, A., & Quaid, E. D. (2021). Understanding learners’ acceptance of high-immersion virtual reality systems: Insights from confirmatory and exploratory PLS-SEM analyses. Computers & Education, 169, 104214. https://doi.org/10.1016/j.compedu.2021.104214
Bassachs, M., Cañabate, D., Nogué, L., Serra, T., Bubnys, R., & Colomer, J. (2020). Fostering Critical Reflection in Primary Education through STEAM Approaches. Education Sciences, 10(12), 384. https://www.mdpi.com/2227-7102/10/12/384
Bloom, B. S. (1956). Taxonomy of educational objectives, handbook I: The cognitive domain. David McKay Co Inc.
Chang, Y.-S., Kuo Jui, H., Chiang, C.-W., & Lugmayr, A. (2019). Applying Mobile Augmented Reality (AR) to Teach Interior Design Students in Layout Plans: Evaluation of Learning Effectiveness Based on the ARCS Model of Learning Motivation Theory. Sensors, 20, 105. https://doi.org/10.3390/s20010105.
Cheng, K.-H. (2017). Reading an augmented reality book: An exploration of learners’ cognitive load, motivation, and attitudes. Australasian Journal of Educational Technology, 33(4). https://doi.org/10.14742/ajet.2820.
Chen, C.-C., & Huang, P.-H. (2020). The effects of STEAM-based mobile learning on learning achievement and cognitive load. Interactive Learning Environments. https://doi.org/10.1080/10494820.2020.1761838
Chu, H.-E., Martin, S. N., & Park, J. (2019). A Theoretical Framework for Developing an Intercultural STEAM Program for Australian and Korean Students to Enhance Science Teaching and Learning. International Journal of Science and Mathematics Education, 17(7), 1251–1266. https://doi.org/10.1007/s10763-018-9922-y
Conradty, C., & Bogner, F. X. (2020). STEAM teaching professional development works: effects on students’ creativity and motivation. Smart Learning Environments, 7(1), 26. https://doi.org/10.1186/s40561-020-00132-9
Costley, J., & Lange, C. (2017). The Mediating Effects of Germane Cognitive Load on the Relationship Between Instructional Design and Students’ Future Behavioral Intention. The Electronic Journal of e-Learning, 15(2)
Curran, V., Gustafson, D. L., Simmons, K., Lannon, H., Wang, C., Garmsiri, M., et al. (2019). Adult learners’ perceptions of self-directed learning and digital technology usage in continuing professional education: An update for the digital age. Journal of Adult and Continuing Education, 25(1), 74–93. https://doi.org/10.1177/1477971419827318.
Dai, H. M., Teo, T., Rappa, N. A., & Huang, F. (2020). Explaining Chinese university students’ continuance learning intention in the MOOC setting: A modified expectation confirmation model perspective. Computers & Education, 150, https://doi.org/10.1016/j.compedu.2020.103850.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.
Dörnyei, Z., & Taguchi, T. (2009). Questionnaires in Second Language Research: Construction, Administration, and Processing (2nd ed.). Routledge. https://doi.org/10.4324/9780203864739.
Dunn, T. J., & Kennedy, M. (2019). Technology Enhanced Learning in higher education; motivations, engagement and academic achievement. Computers & Education, 137, 104–113. https://doi.org/10.1016/j.compedu.2019.04.004.
Ekatushabe, M., Kwarikunda, D., Muwonge, C. M., Ssenyonga, J., & Schiefele, U. (2021). Relations between perceived teacher’s autonomy support, cognitive appraisals and boredom in physics learning among lower secondary school students. International Journal of STEM Education, 8(1), 8. https://doi.org/10.1186/s40594-021-00272-5
Findik-Coşkunçay, D., Alkiş, N., & Özkan-Yildirim, S. (2018). A structural model for students’ adoption of learning management systems: An empirical investigation in the higher education context. Journal of Educational Technology & Society, 21(2), 13–27.
Fletcher, J. D. (2018). Comments and reflections on ITS and STEM education and training. InternationalJournal of STEM Education, 5(1), 16. https://doi.org/10.1186/s40594-018-0106-7.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
Fu, S., Gu, H., & Yang, B. (2020). The affordances of AI-enabled automatic scoring applications on learners’ continuous learning intention: An empirical study in China. British Journal of Educational Technology,. https://doi.org/10.1111/bjet.12995.
Galbis-Córdoba, A., Martí-Parreño, J., & Currás-Pérez, R. (2017). Education Students’ Attitude towards the Use of Gamification for Competencies Development. Journal of E-Learning and Knowledge Society, 13, 1. https://doi.org/10.20368/1971-8829/158
Galeazzo, A., Ortiz-de-Mandojana, N., & Delgado-Ceballos, J. (2021). Green procurement and financial performance in the tourism industry: the moderating role of tourists’ green purchasing behaviour. Current Issues in Tourism, 24(5), 700–716. https://doi.org/10.1080/13683500.2020.1734546
Gao, X., Li, P., Shen, J., & Sun, H. (2020). Reviewing assessment of student learning in interdisciplinary STEM education. International Journal of STEM Education, 7(1), 24. https://doi.org/10.1186/s40594-020-00225-4.
Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572–2593. https://doi.org/10.1111/bjet.12864
Gülen, S., & Yaman S. (2019). The Effect of Integration of STEM Disciplines into Toulmin's Argumentation Model on Students' Academic Achievement, Reflective Thinking, and Psychomotor Skills*. https://doi.org/10.12973/tused.10276.
Gülhan, F., & Şahin, F. (2018, 08/05). The effects of STEAM (STEM+ Art) activities 7th grade students’ academic achievement, STEAM attitude and scientific creativities<p>STEAM (STEM+Sanat) etkinliklerinin 7. sınıf öğrencilerinin akademik başarı, STEAM tutum ve bilimsel yaratıcılıklarına etkisi. Journal of Human Sciences, 15(3), 1675–1699. https://j-humansciences.com/ojs/index.php/IJHS/article/view/5430
Gyamfi, S. A. (2017). Informal Tools in Formal Context: Adoption of Web 2.0 Technologies among Geography Student Teachers in Ghana. International Journal of Education and Development Using Information and Communication Technology, 13(3), 24–40.
Hair, J. F., Hult, G. T. M., Ringe, C. M., & Sarstedt, M. (2016). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). SAGE Publications.
Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2006). Multivariate data analysis. Upper Saddle River, NJ: Pearson Prentice Hall.
Haji, S. A., Moluayonge, G. E., & Park, I. (2017). Teachers’ use of information and communications technology in education: Cameroon secondary schools perspectives. Turkish Online Journal of Educational Technology-TOJET, 16(3), 147–153.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Horng, J.-S., Liu, C.-H., Chou, S.-F., & Huang, Y.-C. (2020). The roles of university education in promoting students’ passion for learning, knowledge management and entrepreneurialism. Journal of Hospitality and Tourism Management, 44, 162–170. https://doi.org/10.1016/j.jhtm.2020.06.005
Huang, W., Huang, W., Diefes-Dux, H., & Imbrie, P. K. (2006). A preliminary validation of Attention, Relevance, Confidence and Satisfaction model-based Instructional Material Motivational Survey in a computer-based tutorial setting. British Journal of Educational Technology, 37(2), 243–259. https://doi.org/10.1111/j.1467-8535.2005.00582.x
Huang, Y., & Liu, X. (2021). The analysis and research of STEAM education based on the TAM algorithm model to improve the learning effectiveness of higher vocational engineering students. Evolutionary Intelligence. https://doi.org/10.1007/s12065-021-00619-5
Huett, J. B. (2006). The Effects of ARCS-based Confidence Strategies on Learner Confidence and Performance in Distance Education. Citations.
Hwang, G.-J., Yang, L.-H., & Wang, S.-Y. (2013). A concept map-embedded educational computer game for improving students’ learning performance in natural science courses. Computers & Education, 69, 121–130. https://doi.org/10.1016/j.compedu.2013.07.008
Kanchanatanee, K., Suwanno, N., & Jarernvongrayab, A. (2014). Effects of Attitude toward Using, Perceived Usefulness, Perceived Ease of Use and Perceived Compatibility on Intention to Use E-Marketing. Journal of Management Research, 6, 3. https://doi.org/10.5296/jmr.v6i3.5573
Karakis, H., Karamete, A., & Okcu, A. (2016). The Effects of a Computer-Assisted Teaching Material, Designed According to the ASSURE Instructional Design and the ARCS Model of Motivation, on Students’ Achievement Levels in a Mathematics Lesson and Their Resulting Attitudes. European Journal of Contemporary Education, 15(1), 105–113. https://doi.org/10.13187/ejced.2016.15.105
Keller, J. M. (1983). Motivational design of instruction. Instructional Design Theories and Models: An Overview of Their Current Status, 1(1983), 383–434.
Keller, J. M. (2009). Motivational design for learning and performance: The ARCS model approach. New York: Springer.
Koc, E. S., & Ontas, T. (2020). A comparative analysis of the 4th and 5th grade social studies curriculum according to revised bloom taxonomy. Cypriot Journal of Educational Sciences, 15(3), 540–553. https://doi.org/10.18844/cjes.v15i3.4931.
Koul, R. B., & Fisher, D. L. (2005). Cultural Background and Students’ Perceptions of Science Classroom Learning Environment and Teacher Interpersonal Behaviour in Jammu. India. Learning Environments Research, 8(2), 195–211. https://doi.org/10.1007/s10984-005-7252-9
Kozlovskiy, Y., Bilyk, V., Zakharyash, O., Voloshyn, M., & Babkina, М. (2021). The information and communication technologies in the learning process of students in the context of cognitive development. Laplage em Revista, 7(3C), 471–476.
Krathwohl, D. R., Bloom, B. S., & Masia, B. B. (1973). Taxonomy of Educational Objectives, the Classification of Educational Goals. Handbook II: Affective Domain. New York: David McKay Co., Inc.
Land, M. H. (2013). 2013/01/01/). Full STEAM Ahead: The Benefits of Integrating the Arts Into STEM. Procedia Computer Science, 20, 547–552. https://doi.org/10.1016/j.procs.2013.09.317
Li, C.-L., Chen, Y.-H., & Li, H.-Y. (2018). Technical College Students’ ARCS Learning Motivation on Hospitality English Vocabulary. International Jouranl of Human Resource Studies, 8(1), 189–207. https://doi.org/10.5296/ijhrs.v8i1.12370
Li, K., & Moore, D. R. (2018). Motivating Students in Massive Open Online Courses (MOOCs) Using the Attention, Relevance, Confidence, Satisfaction (ARCS) Model. Journal of Formative Design in Learning, 2(2), 102–113. https://doi.org/10.1007/s41686-018-0021-9
Lin, K.-Y., Wu, Y.-T., Hsu, Y.-T., & Williams, P. J. (2021). Effects of infusing the engineering design process into STEM project-based learning to develop preservice technology teachers’ engineering design thinking. International Journal of STEM Education, 8(1), 1. https://doi.org/10.1186/s40594-020-00258-9
Lin, P.-Y., Chai, C.-S., Jong, M.S.-Y., Dai, Y., Guo, Y., & Qin, J. (2021). Modeling the structural relationship among primary students’ motivation to learn artificial intelligence. Computers and Education: Artificial Intelligence. https://doi.org/10.1016/j.caeai.2020.100006
Liu, C.-H. (2020). Local and international perspectives of the influence of creative experiences of Chinese traditional culture on revisit intentions. Current Issues in Tourism, 23(1), 17–35. https://doi.org/10.1080/13683500.2018.1564740
Liu, Q., Yu, S., Chen, W., Wang, Q., & Xu, S. (2021). The effects of an augmented reality based magnetic experimental tool on students’ knowledge improvement and cognitive load. Journal of Computer Assisted Learning, 37(3), 645–656. https://doi.org/10.1111/jcal.12513
Lohmöller, J.-B. (1989). Latent Variable Path Modeling with Partial Least Squares. Physica.
Loughlin-Presnal, J., & Bierman, K. L. (2017). How do parent expectations promote child academic achievement in early elementary school? A test of three mediators. Developmental Psychology, 53(9), 1694–1708. https://doi.org/10.1037/dev0000369
Lu, Y., Yeung, W.-J. J., & Treiman, D. J. (2020). Parental migration and children’spsychological and cognitive development in China: differences and mediating mechanisms. Chinese Sociological Review, 52(4), 337–363. https://doi.org/10.1080/21620555.2020.1776600.
Margot, K. C., & Kettler, T. (2019). Teachers’ perception of STEM integration and education: a systematic literature review. International Journal of STEM Education, 6(1), 2. https://doi.org/10.1186/s40594-018-0151-2.
Marín-Marín, J.-A., Moreno-Guerrero, A.-J., Dúo-Terrón, P., & López-Belmonte, J. (2021). STEAM in education: a bibliometric analysis of performance and co-words in Web of Science. International Journal of STEM Education, 8(1), 41. https://doi.org/10.1186/s40594-021-00296-x
Matthews, L. (2017). Applying multigroup analysis in PLS-SEM: A
step-by-step process. In (pp. 219-243).
Mu, R., & De Jong, M. (2018). The psychology of local officials: explaining strategic behavior in the Chinese Target Responsibility System. Journal of Chinese Governance, 3(2), 243–260. https://doi.org/10.1080/23812346.2018.1455413
Munawaroh, M. (2020). The Influence of Problem-Based Learning Model as Learning Method, and Learning Motivation on Entrepreneurial Attitude. International Journal of Instruction, 13(2), 431–444. https://doi.org/10.29333/iji.2020.13230a
Musavi, M., Friess, W. A., James, C., & Isherwood, J. C. (2018). Changing the face of STEM with stormwater research. International Journal of STEM Education, 5(1), 2. https://doi.org/10.1186/s40594-018-0099-2.
Mutambara, D., & Bayaga, A. (2021). Determinants of mobile learning acceptance for STEM education in rural areas. Computers & Education, 160, 104010. https://doi.org/10.1016/j.compedu.2020.104010
Ngah, A. H., Rashid, R. A., Ariffin, N. A., Ibrahim, F., Osman, N. A. A., Kamalrulzaman, N. I., Mohamad, M. F. H., & Harun, N. O. (2021). Fostering Students’ Attitude Towards Online Learning: the Mediation Effect of Satisfaction and Perceived Performance. https://easychair.org/publications/preprint/VMxp
Ocak, G., & Tepe, M., & Olur, B. (2020). Investigation of the objectives of the information technologh and sofeware course to the revised Bloom taxonomy. European Journal of Education Studies, 6(12), 56–68. https://doi.org/10.46827/ejes.v0i0.2857.
Olmedo Moreno, E. M., de Luna, E. B., Olmos Gómez, M. D. C., & López, J. E. (2014). Structural Equations Model (SEM) of a questionnaire on the evaluation of intercultural secondary education classrooms. Suma Psicológica, 21(2), 107–115. https://doi.org/10.1016/S0121-4381(14)70013-X.
Paas, F. (1992). 12/01). Training Strategies for Attaining Transfer of Problem-Solving Skill in Statistics: A Cognitive-Load Approach. Journal of Educational Psychology, 84, 429–434. https://doi.org/10.1037/0022-06184.108.40.2069
Paas, F., Tuovinen, J. E., Tabbers, H., & Van Gerven, P. W. (2003). Cognitive load measurement as a means to advance cognitive load theory. Educational Psychologist, 38(1), 63–71. https://doi.org/10.1207/S15326985EP3801_8
Pabalan, N., Singian, E., Tabangay, L., Jarjanazi, H., Boivin, M. J., & Ezeamama, A. E. (2018). Soil-transmitted helminth infection, loss of education and cognitive impairment in school-aged children: A systematic review and meta-analysis. PLoS Negl Trop Dis, 12(1), e0005523. https://doi.org/10.1371/journal.pntd.0005523.
Pangarso, A., Astuti, E. S., Raharjo, K., & Afrianty, T. W. (2020). Data of innovation ambidexterity as a mediator in the absorptive capacity effect on sustainable competitive advantage. Data in Brief, 29, 105200. https://doi.org/10.1016/j.dib.2020.105200
Peter, J. P. (1981). Construct validity: A review of basic issues and marketing practices. Journal of Marketing Research, 18(2), 133–145. https://doi.org/10.2307/3150948
Quaid, E., Pack, A., Barrett, A., & Zhou, L. (2020, 12/13). Students' intention to use high-immersion virtual reality systems for learning paragraph structure: a PLS-SEM exploratory study. 291–297. https://doi.org/10.14705/rpnet.2020.48.1203
Radhamani, R., Kumar, D., Nizar, N., Achuthan, K., Nair, B., & Diwakar, S. (2021). What virtual laboratory usage tells us about laboratory skill education pre- and post-COVID-19: Focus on usage, behavior, intention and adoption. Education and Information Technologies. https://doi.org/10.1007/s10639-021-10583-3.
Ramma, Y., Bholoa, A., Watts, M., & Nadal, P. S. (2018). Teaching and learning physics using technology: Making a case for the affective domain. Education Inquiry, 9(2), 210–236. https://doi.org/10.1080/20004508.2017.1343606.
Sam Liu, C.-H. (2017). Remodelling progress in tourism and hospitality students’ creativity through social capital and transformational leadership. Journal of Hospitality, Leisure, Sport & Tourism Education, 21, 69–82. https://doi.org/10.1016/j.jhlste.2017.08.003.
Shernoff, D. J., Sinha, S., Bressler, D. M., & Ginsburg, L. (2017). Assessing teacher education and professional development needs for the implementation of integrated approaches to STEM education. International Journal of STEM Education, 4(1), 1–16. https://doi.org/10.1186/s40594-017-0068-1.
Shiau, S., Huang, C.-Y., Yang, C.-L., & Juang, J.-N. (2018). A Derivation of Factors Influencing the Innovation Diffusion of the OpenStreetMap in STEM Education. Sustainability, 10, 10. https://doi.org/10.3390/su10103447
Simpson, E. J. (1972). The Classification of Educational Objectives in the Psychomotor Domain. Gryphon House.
Su, C.-H. (2019). The Effect of Users’ Behavioral Intention on Gamification Augmented Reality in Stem (Gar-Stem) Education. Journal of Baltic Science Education, 18(3), 450–465. https://doi.org/10.33225/jbse/19.18.450
Su, C. H., & Cheng, C. H. (2015). A mobile gamification learning system for improving the learning motivation and achievements. Journal of Computer Assisted Learning, 31(3), 268–286. https://doi.org/10.1111/jcal.12088
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285.
Sweller, J., Van Merrienboer, J. J., & Paas, F. G. (1998a). Cognitive architecture and instructional design. Educational Psychology Review, 10(3), 251–296.
Sweller, J., van Merrienboer, J. J. G., & Paas, F. G. W. C. (1998b). Cognitive Architecture and Instructional Design. Educational Psychology Review, 10(3), 251–296. https://doi.org/10.1023/A:1022193728205
Tan, D. Y., & Cheah, C. W. (2021). Developing a gamified AI-enabled online learning application to improve students’ perception of university physics. Computers and Education: Artificial Intelligence, 2, 100032. https://doi.org/10.1016/j.caeai.2021.100032
Teo, T. (2009). Modelling technology acceptance in education: A study of pre-service teachers. Computers & Education, 52(2), 302–312. https://doi.org/10.1016/j.compedu.2008.08.006
Trujillo, L. T. (2019). Mental effort and information-processing costs are inversely related to global brain free energy during visual categorization. Frontiers in Neuroscience, 13, 1292. https://doi.org/10.3389/fnins.2019.01292
Voorhees, C. M., Brady, M. K., Calantone, R., & Ramirez, E. (2016). Discriminant validity testing in marketing: an analysis, causes for concern, and proposed remedies. Journal of the Academy of Marketing Science, 44(1), 119–134. https://doi.org/10.1007/s11747-015-0455-4
Wahyudi, S., Joyoatmojo, S., & Sawiji, H. (2017). Learning Model of Attention, Relevance, Confidence, Satisfaction (ARCS) supported by video tutorial to improve the students’ learning motivation in vocational high school. Advances in Social Science Education and Humanities Research (ASSEHR), 158, 603–611. https://doi.org/10.2991/ictte-17.2017.72
Wahono, B., Lin, P.-L., & Chang, C.-Y. (2020). Evidence of STEM enactment effectiveness in Asian student learning outcomes. International Journal of STEM Education, 7(1), 36. https://doi.org/10.1186/s40594-020-00236-1.
Weng, F., Yang, R.-J., Ho, H.-J., & Su, H.-M. (2018). A TAM-Based Study of the Attitude towards Use Intention of Multimedia among School Teachers. Applied System Innovation, 1, 3. https://doi.org/10.3390/asi1030036
Wu, B., & Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67, 221–232. https://doi.org/10.1016/j.chb.2016.10.028
Yafie, E., Nirmala, B., Kurniawaty, L., Bakri, T. S. M., Hani, A. B., & Setyaningsih, D. (2020). Supporting Cognitive Development through Multimedia Learning and Scientific Approach: An Experimental Study in Preschool. Universal Journal of Educational Research, 8(11C), 113–123. https://doi.org/10.13189/ujer.2020.082313.
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0.
Zhang, S.-N., Li, Y.-Q., Liu, C.-H., & Ruan, W.-Q. (2019). Critical factors in the identification of word-of-mouth enhanced with travel apps: the moderating roles of Confucian culture and the switching cost view. Asia Pacific Journal of Tourism Research, 24(5), 422–442. https://doi.org/10.1080/10941665.2019.1572630
Zhu, S., Yang, H. H., MacLeod, J., Yu, L., & Wu, D. (2019). Investigating Teenage Students’ Information Literacy in China: A Social Cognitive Theory Perspective. The Asia-Pacific Education Researcher, 28(3), 251–263. https://doi.org/10.1007/s40299-019-00433-9
Zorluoglu, S. L., Bagriyanik, K. E., & Sahintürk, A. (2019). Analyze of the Science and Technology Course TEOG Questions Based on the Revised Bloom Taxonomy and Their Relation between the Learning Outcomes of the Curriculum. International Journal of Progressive Education, 15(2), 104–117.