Exploring pre-service biology teachers’ intention to teach genetics using an AI intelligent tutoring - based system

References

• Abdel-Maksoud, N. F. (2018). The relationship between students’ satisfaction in the LMS" Acadox" and their perceptions of its usefulness, and ease of use. Journal of Education and Learning, 7(2), 184–190. https://doi.org/10.5539/jel.v7n2p184
   Google Scholar
• Abimbola, I. O. (2015). Learning how to learn for perfect understanding. Bamitex Printing and Publishing.
   Google Scholar
• Adebayo, I., Oladejo, A. I., & Okebukola, P. A. (2022). Examining the relative effectiveness of CTCA in improving secondary school students’ achievement in genetics. In Proceedings of the NARST 95th Annual International Conference: Unity and Inclusion for Global Scientific Literacy: Invite as a Community. Unite as a Community, Vancouver, BC, Canada (pp. 27–30).
   Google Scholar
• Adelana, O. P., & Akinyemi, A. L. (2021). Artificial intelligence-based tutoring systems utilization for learning: A survey of senior secondary students’ awareness and readiness in Ijebu- Ode, Ogun State. Unizik Journal of Educational Research and Policy Studies, 9, 16–28.
   Google Scholar
• Adelana, O. P., Akinsulure, A. O., Ajose, M. A., & Ishola, A. M. (2023a). Perceptions of genetics difficulty among science students in Nigeria. Journal of Science and Mathematics Letters, 11(1), 51–58.
   Google Scholar
• Adelana, O. P., Ayanwale, M. A., Ishola, A. M., Oladejo, A. I., & Adewuyi, H. O. (2023b). Exploring pre-service teachers’ intention to use virtual reality: A mixed method approach. Computers & Education: X Reality, 3, 100045. https://doi.org/10.1016/j.cexr.2023.100045
   Google Scholar
• Adelana, O. P., Ishola, A. M., & Adeeko, O. (2021). Development and validation of instructional package for teaching and learning of genetics in secondary schools. Asian Journal of Assessment in Teaching and Learning, 11(2), 32–41.
   Google Scholar
• Agboghoroma, T. E., & Oyovwi, E. O. (2015). Evaluating effect of students’ academic achievement on identified difficult concepts in senior secondary school biology in delta state. Journal of Education and Practice, 6(30), 117–125.
   Google Scholar
• Agudo-Peregrina, Á. F., Hernández-García, Á., & Pascual-Miguel, F. J. (2014). Behavioral intention, use behavior and the acceptance of electronic learning systems: Differences between higher education and lifelong learning. Computers in Human Behavior, 34(0), 301e314. https://doi.org/10.1016/j.chb.2013.10.035
   Google Scholar
• Ajanaku, A. A., Gambari, A. I., & Ibrahim, I. K. (2019). Development and assessment of gamification instructional package on genetic concepts for senior secondary schools achievement and gender in Minna, Nigeria. Conference Proceedings of the AITIE 3rd International Conference and Workshop on Innovation, Technology and Education (ICWITE), Abuja.
   Google Scholar
• Ajayi, O. A., & Adelana, O. P. (2020). Effectiveness of multimedia self-learning package in teaching and learning of genetics in secondary schools. Journal of Psychometry and Assessment Techniques, 1(1), 119–120.
   Google Scholar
• Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckmann (Eds.), Action control: From cognition to behavior (p. 11–39). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-69746-3_2
   Google Scholar
• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
   Web of Science ®Google Scholar
• Ajzen, I., & Fishbein, M. (2005). The influence of attitudes on behavior. In B. T. Johnson & M. P. Zanna (Eds.), The handbook of attitudes (p. 173–221). Lawrence Erlbaum Associates.
   Google Scholar
• Akinnubi, R. T., Oketayo, O. O., Akinwande, D. D., & Ifedayo, O. A. (2012). Student-lecturer variables and low performance of students in genetics: A case study of course in Adeyemi College of Education, Ondo State, Nigeria. Journal of Research in Education and Society, 3(2), 78–82.
   Google Scholar
• Akinyemi, A. L., Adelana, O. P., & Olurinola, O. D. (2022). Use of infographics as teaching and learning tools: Survey of pre-service teachers’ knowledge and readiness in a Nigerian university. Journal of ICT in Education, 9(1), 117–130. https://doi.org/10.37134/jictie.vol9.1.10.2022
   Google Scholar
• Alabi, H., & Abimbola, I. O. (2022). Score level, text structures and senior secondary school students’ achievement in genetics in Ilorin, Nigeria. International Journal of Educational Research Review, 7(4), 198–209.
   Google Scholar
• Amusa, J. O., & Ayanwale, M. A. (2021). Partial least square modeling of personality traits and academic achievement in physics. Asian Journal of Assessment in Teaching and Learning, 11(2), 77–92. https://doi.org/10.37134/ajatel.vol11.2.8.2021
   Google Scholar
• Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423. https://doi.org/10.1504/EJIM.2021.114662
   Web of Science ®Google Scholar
• Annetta, L. A., Minogue, J., Holmes, S. Y., & Cheng, M. T. (2009). Investigating the impact of video games on high school students’ engagement and learning about genetics. Computers & Education, 53(1), 74–85. https://doi.org/10.1016/j.compedu.2008.12.020
   Web of Science ®Google Scholar
• Aptyka, H., & Großschedl, J. (2022). Analyzing pre-service biology teachers’ intention to teach evolution using the theory of planned behavior. Evolution, 15(1), 16. https://doi.org/10.1186/s12052-022-00175-1
   PubMedGoogle Scholar
• Arthur, S. (2013). Evolution acceptance among pre-service primary teachers. Evolution: Education and Outreach, 6(20), 1–11. https://doi.org/10.1186/1936-6434-6-20
   Google Scholar
• Asiri, M. J. (2019). Do teachers’ attitudes, perception of usefulness, and perceived social influences predict their behavioral intentions to use gamification in EFL classrooms? Evidence from the Middle East. International Journal of Education and Practice, 7(3), 112–122. https://doi.org/10.18488/journal.61.2019.73.112.122
   Google Scholar
• Aslan, A., & Zhu, C. (2017). Investigating variables predicting Turkish pre‐service teachers’ integration of ICT into teaching practices. British Journal of Educational Technology, 48(2), 552–570. https://doi.org/10.1111/bjet.12437
   Web of Science ®Google Scholar
• Auwalu, R. A., Mohd, E. T., & Muhammad, B. G. (2014). Academic achievement in biology with suggested solutions in selected secondary schools in Kano State, Nigeria. International Journal of Education and Research, 2(11), 215–224.
   Google Scholar
• Ayanwale, M. A. (2023). Evidence from Lesotho Secondary Schools on Students’ Intention to Engage in Artificial Intelligence Learning [Paper presentation]. IEEE Africon, Nairobi, Kenya. pp. 1–6. https://doi.org/10.1109/AFRICON55910.2023.10293644
   Google Scholar
• Ayanwale, M. A., Molefi, R. R., & Matsie, N. (2023a). Modelling secondary school students’ attitudes toward TVET subjects using social cognitive and planned behavior theories. Social Sciences & Humanities Open, 8(1), 100478. https://doi.org/10.1016/j.ssaho.2023.100478
   Google Scholar
• Ayanwale, M. A., Sanusi, I. T., Adelana, O. P., Aruleba, K., & Oyelere, S. S. (2022). Teachers’ readiness and intention to teach artificial intelligence in schools. Computers and Education: Artificial Intelligence, 3, 100099. https://doi.org/10.1016/j.caeai.2022.100099
   Google Scholar
• Ayanwale, M. A., Sanusi, I. T., Molefi, R. R., & Otunla, A. O. (2023b). A structural equation approach and modelling of pre-service teachers’ perspectives of cybersecurity education. Education and Information Technologies, 6, 1–29. https://doi.org/10.1007/s10639-023-11973-5
   Google Scholar
• Bagozzi, R. P. (1981). Attitudes, intentions, and behavior: A test of some key hypotheses. Journal of Personality and Social Psychology, 41(4), 607–627. https://doi.org/10.1037/0022-3514.41.4.607
   Web of Science ®Google Scholar
• Bahcivan, E., Gurer, M. D., Yavuzalp, N., & Akayoglu, S. (2019). Investigating the relations among pre-service teachers’ teaching/learning beliefs and educational technology integration competencies: A structural equation modeling study. Journal of Science Education and Technology, 28(5), 579–588. https://doi.org/10.1007/s10956-019-09788-6
   Web of Science ®Google Scholar
• Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215. https://doi.org/10.1037/0033-295X.84.2.191
   PubMed Web of Science ®Google Scholar
• Bao, Y., Xiong, T., Hu, Z., & Kibelloh, M. (2013). Exploring gender differences on general and specific computer self-efficacy in mobile learning adoption. Journal of Educational Computing Research, 49(1), 111–132. https://doi.org/10.2190/EC.49.1.e
   Web of Science ®Google Scholar
• Barker, L. J., & Aspray, W. (2006). The state of research on girls and IT. In J. M. Cohoon & W. Aspray (Eds.), Women and information technology (pp. 3–54). MIT Press.
   Google Scholar
• Bingimlas, K. A. (2009). Barriers to the successful integration of ICT in teaching and learning environments: A review of the literature. Eurasia Journal of Mathematics, Science and Technology Education, 5, 235–245. https://doi.org/10.12973/ejmste/75275
   Google Scholar
• Can, H., & Hiğde, E. (2022). Pre-service teachers’ intentions to implement STEM activities. Shanlax International Journal of Education, 10(S1-Aug), 164–178. https://doi.org/10.34293/education.v10iS1-Aug.4956
   Google Scholar
• Chen, X., Xie, H., & Hwang, G. J. (2020a). A Multi-perspective study on artificial intelligence in education: Grants, conferences, journals, software tools, institutions, and researchers. Computers and Education: Artificial Intelligence, 1, 100005. https://doi.org/10.1016/j.caeai.2020.100005
   Google Scholar
• Cheng, E. W. L. (2018). Choosing between the theory of planned behavior (TPB) and the technology acceptance model (TAM). Educational Technology Research and Development, 67(1), 21–37. https://doi.org/10.1007/s11423-018-9598-6
   Web of Science ®Google Scholar
• Cheung, S.-F., & Chan, D. K.-S. (2000). The role of perceived behavioral control in predicting human behavior: A meta-analytic review of studies on the theory of planned behavior. Unpublished manuscript, Chinese University of Hong Kong. https://doi.org/10.13140/RG.2.2.31093.01766
   Google Scholar
• Choden, T., & Kijkuakul, S. (2020). Blending problem based learning with scientific argumentation to enhance students’ understanding of basic genetics. International Journal of Instruction, 13(1), 445–462. https://doi.org/10.29333/iji.2020.13129a
   Web of Science ®Google Scholar
• Choi, S., Jang, Y., & Kim, H. (2023). Influence of pedagogical beliefs and perceived trust on teachers’ acceptance of educational artificial intelligence tools. International Journal of Human–Computer Interaction, 39(4), 910–922. https://doi.org/10.1080/10447318.2022.2049145
   Web of Science ®Google Scholar
• Cimer, A. (2012). What makes biology learning difficult and effective: Students’ views. Educational Research and Reviews, 7(3), 61.
   Google Scholar
• Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates, Publishers.
   Google Scholar
• Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159. https://doi.org/10.1037//0033-2909.112.1.155
   PubMed Web of Science ®Google Scholar
• Corbett, A., Kauffman, L., Maclaren, B., Wagner, A., & Jones, E. (2010). A cognitive tutor for genetics problem solving: Learning gains and student modeling. Journal of Educational Computing Research, 42(2), 219–239. https://doi.org/10.2190/EC.42.2.e
   Web of Science ®Google Scholar
• Cuhadar, C. (2018). Investigation of pre-service teachers’ levels of readiness to technology integration in education. Contemporary Educational Technology, 9(1), 61–75. https://doi.org/10.30935/cedtech/6211
   Google Scholar
• Cukurova, M., Khan-Galaria, M., Millán, E., & Luckin, R. (2021). A learning analytics approach to monitoring the quality of online one-to-one tutoring. Journal of Learn. Anal, 1, 1–10. https://doi.org/10.35542/osf.io/qfh7z
   Google Scholar
• D’Mello, S., & Graesser, A. (2012). Dynamics of affective states during complex learning. Learning and Instruction, 22(2), 145–157. https://doi.org/10.1016/j.learninstruc.2011.10.001
   Web of Science ®Google Scholar
• Danmole, B. T., & Lameed, S. N. (2014). Exploring annotated drawing for improving Nigerian secondary school students’ achievement in genetics. International Journal of Biology Education, 3(1b), 1–11. https://doi.org/10.20876/ijobed.48555
   Google Scholar
• Darmansyah, D., Fianto, B. A., Hendratmi, A., & Aziz, P. F. (2020). Factors determining behavioral intentions to use Islamic financial technology: Three competing models. Journal of Islamic Marketing, 12(4), 794–812. https://doi.org/10.1108/JIMA-12-2019-0252
   Web of Science ®Google Scholar
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–339. https://doi.org/10.2307/249008
   Web of Science ®Google Scholar
• Deniz, H., & Borgerding, L. A. (2018). Evolution education around the globe. Springer. https://doi.org/10.1007/978-3-319-90939-4
   Google Scholar
• Deniz, H., & Sahin, E. A. (2016). Exploring the factors related to acceptance of evolutionary theory among Turkish preservice biology teachers and the relationship between acceptance and teaching preference. Electronic Journal of Science Education, 20(4), 21–43. https://doi.org/10.1002/tea.20223
   Google Scholar
• Dogru-Atay, P., & Tekkaya, C. (2008). Promoting students’ learning in genetics with the learning cycle. The Journal of Experimental Education, 76(3), 259–280. https://doi.org/10.3200/JEXE.76.3.259-280
   Web of Science ®Google Scholar
• Dorjee, K., Baxi, S. M., Reingold, A. L., & Hubbard, A. (2017). Risk of cardiovascular events from current, recent, and cumulative exposure to abacavir among persons living with HIV who were receiving antiretroviral therapy in the United States: A cohort study. BMC Infectious Diseases, 17(1), 1–12. https://doi.org/10.1186/s12879-017-2808-8
   PubMedGoogle Scholar
• Du Boulay, B. (2016). Artificial intelligence as an effective classroom assistant. IEEE Intelligent Systems, 31(6), 76–81. https://doi.org/10.1109/MIS.2016.93
   Web of Science ®Google Scholar
• Ernest, T. (2015). The impact of computer-aided instruction on student achievement. Education Theses, Dissertations and Projects, 127
   Google Scholar
• Ertel, W. (2018). Introduction to artificial intelligence (pp. vii). Springer. https://doi.org/10.1007/978-3-319-58487-4
   Google Scholar
• Ertmer, P. A., Addison, P., Lane, M., Ross, E., & Woods, D. (1999). Examining teachers’ beliefs about the role of technology in the elementary classroom. Journal of Research on Computing in Education, 32(1), 54–72. https://doi.org/10.1080/08886504.1999.10782269
   Google Scholar
• Ertmer, P. A., Ottenbreit-Leftwich, A. T., Sadik, O., Sendurur, E., & Sendurur, P. (2012). Teacher beliefs.
   Google Scholar
• Etobro, A. B., & Banjoko, S. O. (2017). Misconceptions of genetics concepts among pre-service teachers. Global Journal of Educational Research, 16(2), 121–128. https://doi.org/10.4314/gjedr.v16i2.6
   Google Scholar
• Federal Republic of Nigeria. (2014). National policy on Education. NERDC.
   Google Scholar
• 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
   Web of Science ®Google Scholar
• Geisser, S. (1974). A predictive approach to the random effects model. Biometrika, 61(1), 101–107. https://doi.org/10.1093/biomet/61.1.101
   Web of Science ®Google Scholar
• Glavee-Geo, R., Shaikh, A. A., & Karjaluoto, H. (2017). Mobile banking services adoption in Pakistan: Are there gender differences? International Journal of Bank Marketing, 35(7), 1090–1114. https://doi.org/10.1108/IJBM-09-2015-0142
   Web of Science ®Google Scholar
• Goodhue, D.L., Lewis, W., & Thompson, R. (2012). Does PLS have advantages for small sample size or non-normal data? MIS Quarterly, 36(3), 981–1001. https://doi.org/10.2307/41703490
   Web of Science ®Google Scholar
• Goy, A., Nishtar, S., Dzau, V., Balatbat, C., & Diabo, R. (2019). Health and healthcare in the fourth industrial revolution: Global Future Council on the future of health and healthcare, 2016-2018. World Economic Forum.
   Google Scholar
• Greisel, M., Wekerle, C., Wilkes, T., Stark, R., & Kollar, I. (2023). Pre-service teachers’ evidence-informed reasoning: Do attitudes, subjective norms, and self-efficacy facilitate the use of scientific theories to analyze teaching problems? Psychology Learning & Teaching, 22(1), 20–38. https://doi.org/10.1177/14757257221113942
   Google Scholar
• Griffith, J. A., & Brem, S. K. (2004). Teaching evolutionary biology: Pressures, stress, and coping. Journal of Research in Science Teaching, 41(8), 791–809. https://doi.org/10.1002/tea.20027
   Web of Science ®Google Scholar
• Grosschedl, J., Konnemann, C., & Basel, N. (2014). Pre-service biology teachers’ acceptance of evolutionary theory and their preference for its teaching. Evol Educ Outreach, 7(1), 18. https://doi.org/10.1186/s12052-014-0018-z
   Google Scholar
• Guilherme, A. (2019). AI and education: The importance of teacher and student relations. AI & Society, 34(1), 47–54. https://doi.org/10.1007/s00146-017-0693-8
   Web of Science ®Google Scholar
• Gusmalini, A., & Wulandari, S. (2020, October). Identification of misconceptions and causes of student misconceptions on genetics concept with CRI method. Journal of Physics: Conference Series, 1655(1), 012053. https://doi.org/10.1088/1742-6596/1655/1/012053
   Google Scholar
• Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis. (7th Edition). Pearson.
   Google Scholar
• Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS‐SEM) (3rd ed.). Sage.
   Google Scholar
• Hair, J. F., Jr, Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS- SEM using confirmatory composite analysis. Journal of Business Research, 109, 101–110. https://doi.org/10.1016/j.jbusres.2019.11.069
   Web of Science ®Google Scholar
• Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152. https://doi.org/10.2753/MTP1069-6679190202
   Google Scholar
• 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
   Web of Science ®Google Scholar
• Holstein, K., McLaren, B. M., & Aleven, V. (2018). Student learning benefits of a mixed-reality teacher awareness tool in AIEnhanced classrooms. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10947 , 154–168. https://doi.org/10.1007/978-3-319-93843-1_12
   Google Scholar
• Hou, M., Lin, Y., Shen, Y., & Zhou, H. (2022). Explaining pre-service teachers’ intentions to use technology-enabled learning: An extended model of the theory of planned behavior. Frontiers in Psychology, 13, 900806. https://doi.org/10.3389/fpsyg.2022.900806
   PubMed Web of Science ®Google Scholar
• Hsieh, P.-J. (2015). Physicians’ acceptance of electronic medical records exchange: An extension of the decomposed TPB model with institutional trust and perceived risk. International Journal of Medical Informatics, 84(1), 1–14. https://doi.org/10.1016/j.ijmedinf.2014.08.008
   PubMed Web of Science ®Google Scholar
• Hu, H., & Garimella, U. (2014). iPads for STEM Teachers: A case study on perceived usefulness, perceived proficiency, intention to adopt, and integration in K-12 instruction. Journal of Educational Technology Development and Exchange, 7(1), 49–66. https://doi.org/10.18785/jetde.0701.04
   Google Scholar
• Huang, F., Teo, T., & Zhou, M. (2020). Chinese students’ intentions to use the Internet-based technology for learning. Educational Technology Research and Development, 68(1), 575–591. https://doi.org/10.1007/s11423-019-09695-y
   Web of Science ®Google Scholar
• Humphrey, S. E., & Aime, F. (2014). Team microdynamics: Toward an organizing approach to teamwork. Academy of Management Annals, 8(1), 443–503. https://doi.org/10.5465/19416520.2014.904140
   Web of Science ®Google Scholar
• Hwang, G. J., & Tu, Y. F. (2021). Roles and research trends of artificial intelligence in mathematics education: A bibliometric mapping analysis and systematic review. Mathematics, 9(6), 584. https://doi.org/10.3390/math9060584
   Web of Science ®Google Scholar
• Hwang, G. J., Hung, P. H., Chen, N. S., & Liu, G. Z. (2014). Mind tool-assisted in-field learning (MAIL): An Advanced ubiquitous learning project in Taiwan. Journal of Educational Technology & Society, 17(2), 4–16.
   Web of Science ®Google Scholar
• Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020c). Vision, challenges, roles and research issues of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1, 100001. https://doi.org/10.1016/j.caeai.2020.100001
   Google Scholar
• Iddrisu, A., Ondigi, S., Muchiri, M. M., & Kiio, M. N. (2019). Senior high schools’ students’ perception of computer-aided instruction in north east region of Ghana. International Journal of Innovative Research and Advanced Studies (IJIRAS), 6(8), 19.
   Google Scholar
• Ismail, S. A. A., Almekhlafi, A. G., & Al-Mekhlafy, M. H. (2010). Teachers’ perceptions of the use of technology in teaching languages in United Arab Emirates’ schools. International Journal for Research in Education, 27(1), 37–56.
   Google Scholar
• Jamovi project. (2021). jamovi. (Version 2.2.3) [Computer Software]. https://www.jamovi.org
   Google Scholar
• Jatileni, C. N., Sanusi, I. T., Olaleye, S. A., Ayanwale, M. A., Agbo, F. J., & Oyelere, P. B. (2023). Artificial intelligence in compulsory level of education: Perspectives from Namibian in‑service teachers. Education and Information Technologies. https://doi.org/10.1007/s10639-023-12341-z
   Web of Science ®Google Scholar
• Jekayinfa, A. A., Yahaya, L. A., Yusuf, A., Ajidagba, U. A., Oniye, A. O., Oniyangi, S. O., & Ibraheem, T. O. (2012). Lecturers’ assessment of teaching practice exercise in Nigerian universities. Journal of Education and Practice, 3(4), 79–86.
   Google Scholar
• Kantahan, S., Junpeng, P., Punturat, S., Tang, K. N., Gochyyev, P., & Wilson, M. (2020). Designing and verifying a tool for diagnosing scientific misconceptions in genetics topic. International Journal of Evaluation and Research in Education (IJERE), 9(3), 564–571. https://doi.org/10.11591/ijere.v9i3.20544
   Google Scholar
• Karsenti, T. (2019). Artificial intelligence in education: The urgent need to prepare teachers for tomorrow’s schools. Formation et Profession, 27(1), 105–111. https://doi.org/10.18162/fp.2018.a16
   Google Scholar
• Kay, R. H. (2008). Exploring gender differences in computer-related behaviour: Past, present, and future. In T. T. Chen & I. Chen (Eds.), Social information technology: Connecting society and cultural issues (pp. 12–30). Information Science Reference.
   Google Scholar
• Kay, R. H. (2009). Examining gender differences in attitudes toward interactive classroom communications systems (ICCS). Computers & Education, 52(4), 730–740. https://doi.org/10.1016/j.compedu.2008.11.015
   Web of Science ®Google Scholar
• Khor, K. S., & Hazen, B. T. (2017). Remanufactured products purchase intentions and behaviour: Evidence from Malaysia. International Journal of Production Research, 55(8), 2149–2162. https://doi.org/10.1080/00207543.2016.1194534
   Web of Science ®Google Scholar
• Khukalenko, I. S., Kaplan-Rakowski, R., An, Y., & Iushina, V. D. (2022). Teachers’ perceptions of using virtual reality technology in classrooms: A large-scale survey. Education and Information Technologies, 27(8), 11591–11613. https://doi.org/10.1007/s10639-022-11061-0
   PubMed Web of Science ®Google Scholar
• Kilic, D. S. (2012). Biyoloji oğretmen adaylarinin evrim oğretimi niyetleri [Pre-service biology teacher’s intention to teach evolution]. Hacet Univ Eğit Fakultesi Derg, 42, 250–261.
   Google Scholar
• Kim, N. J., & Kim, M. K. (2022). Teacher’s perceptions of using an artificial intelligence-based educational tool for scientific writing. Frontiers in Education, 7, 755914. https://doi.org/10.3389/feduc.2022.755914
   Google Scholar
• Kline, R. B. (2015). Principles and practice of structural equation modeling. Guilford publications.
   Google Scholar
• Knippels, M. C. P., Waarlo, A. J., & Boersma, K. T. (2005). Design criteria for learning and teaching genetics. Journal of Biological Education, 39(3), 108–112. https://doi.org/10.1080/00219266.2005.9655976
   Web of Science ®Google Scholar
• Kock, N. (2014). Advanced mediating effects tests, multi-group analyses, and measurement model assessments in PLS-based SEM. International Journal of e-Collaboration, 10(1), 1–13. https://doi.org/10.4018/ijec.2014010101
   Google Scholar
• Kock, N. (2017). Common method bias: A full collinearity assessment method for PLS-SEM. In H. Latan & R. Noonan (Eds.), Partial least squares path modeling. Springer. https://doi.org/10.1007/978-3-319-64069-3_11
   Google Scholar
• Kock, N. (2021). WarpPLS 7.0 user manual. ScriptWarp Systems.
   Google Scholar
• Kock, N., & Gaskins, L. (2014). The mediating role of voice and accountability in the relationship between Internet diffusion and government corruption in Latin America and Sub-Saharan Africa. Information Technology for Development, 20(1), 23–43. https://doi.org/10.1080/02681102.2013.832129
   Web of Science ®Google Scholar
• Kock, N., & Lynn, G. S. (2012). Lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations. Journal of the Association for Information Systems, 13(7), 546–580. https://doi.org/10.17705/1jais.00302
   Web of Science ®Google Scholar
• Koohang, A. A. (1989). A study of attitudes toward computers: Anxiety, confidence, liking and perception of usefulness. Journal of Research on Computing in Education, 22(2), 137–150. https://doi.org/10.1080/08886504.1989.10781909
   Google Scholar
• Latifi, S., Noroozi, O., & Talaee, E. (2020). Worked example or scripting? Fostering students’ online argumentative peer feedback, essay writing and learning. Interact. Interactive Learning Environments, 31(2), 655–669. https://doi.org/10.1080/10494820.2020.1799032
   Web of Science ®Google Scholar
• Lewis, J., & Wood-Robinson, C. (2000). Genes, chromosomes, cell division and inheritance—Do students see any relationship? International Journal of Science Education, 22(2), 177–195. https://doi.org/10.1080/095006900289949
   Web of Science ®Google Scholar
• Lin, H. C., Tu, Y. F., Hwang, G. J., & Huang, H. (2021). From precision education to precision medicine. Educational Technology & Society, 24(1), 123–137.
   Web of Science ®Google Scholar
• Marbach‐Ad, G., Rotbain, Y., & Stavy, R. (2008). Using computer animation and illustration activities to improve high school students’ achievement in molecular genetics. Journal of Research in Science Teaching, 45(3), 273–292. https://doi.org/10.1002/tea.20222
   Web of Science ®Google Scholar
• Maynard, A. D. (2015). Navigating the fourth industrial revolution. Nature Nanotechnology, 10(12), 1005–1006. https://doi.org/10.1038/nnano.2015.286
   PubMed Web of Science ®Google Scholar
• Mitrovíc, A. (1998). Experiences in implementing constraint-based modeling in SQL-Tutor. In International conference on intelligent tutoring systems (pp. 414–423). Springer. https://doi.org/10.1007/3-540-68716-5_47
   Google Scholar
• Molefi, R. R., & Ayanwale, M. A. (2023). Using composite structural equation modeling to examine high school teachers’ acceptance of e-learning after Covid-19. New Trends and Issues Proceedings on Humanities and Social Sciences, 10(1), 01–11. https://doi.org/10.18844/prosoc.v10i1.8837
   Google Scholar
• Mueller, Ashley L., Knobloch, Neil A., OrKthryn S.,. (2015). Exploring the Effects of Active Learning on High School Students’ Outcomes and Teachers’ Perceptions of Biotechnology and Genetics Instruction.Journal of Agricultural Education, 56(2), 138–152. https://doi.org/10.5032/jae.2015.02138
   Google Scholar
• Musa, S., & Bello, G. (2022). Effects of metaphor instructional strategy on senior school students achievement in genetics in Ilorin, Nigeria. International Journal of Educational Research Review, 7(3), 165–175. https://doi.org/10.24331/ijere.1098303
   Google Scholar
• Nudelma, N. S., & Okechukwu, R. N. (2006). The effect of concept mapping and problem- solving teaching strategies on achievement in genetics among Nigerian secondary school students. African Journal of Educational Studies in Mathematics and Sciences, 4, 93–98.
   Google Scholar
• Odufuwa, T. T., Adelana, O. P., & Adekunjo, M. A. (2022). Assessment of senior secondary students’ perceptions and career interest in science, technology, engineering and mathematics (STEM) in Ijebu-Ode Local Government Area, Ogun State. Journal of Science, Technology, Mathematics and Education, 18(1), 147.
   Google Scholar
• Oladele, J. I., Ndlovu, M., & Ayanwale, M. A. (2022). Computer adaptive-based learning and assessment for enhancing STEM education in Africa: A fourth industrial revolution possibility. In B. Chirinda, K. Luneta, & A. Uworwabayeho (Eds.), Mathematics education in Africa. Springer. https://doi.org/10.1007/978-3-031-13927-7_8
   Google Scholar
• Onowugbeda, F. U. (2020). Variation and genetics as difficult topics for secondary school students in biology: Exploring the impact of indigenous (cultural) knowledge for better understanding. Breaking Barriers to Learning of Science: The CTC Approach, 140–154.
   Google Scholar
• Orser, B., Riding, A., & Stanley, J. (2012). Perceived career challenges and response strategies of women in the advanced technology sector. Entrepreneurship & Regional Development, 24(1-2), 73–93. https://doi.org/10.1080/08985626.2012.637355
   Web of Science ®Google Scholar
• Osman, E., BouJaoude, S., & Hamdan, H. (2017). An investigation of Lebanese G7-12 students’ misconceptions and difficulties in genetics and their genetics literacy. International Journal of Science and Mathematics Education, 15(7), 1257–1280. https://doi.org/10.1007/s10763-016-9743-9
   Web of Science ®Google Scholar
• Ozcan, A. (2014). Mobile phones democratize and cultivate next-generation imaging, diagnostics and measurement tools. Lab on a Chip, 14(17), 3187–3194. https://doi.org/10.1039/c4lc00010b
   PubMed Web of Science ®Google Scholar
• Pan, J., Ding, S., Wu, D., Yang, S., & Yang, J. (2019). Exploring behavioural intentions toward smart healthcare services among medical practitioners: A technology transfer perspective. International Journal of Production Research, 57(18), 5801–5820. https://doi.org/10.1080/00207543.2018.1550272
   Web of Science ®Google Scholar
• Peng, D. X., & Lai, F. (2012). Using partial least squares in operations management research: A practical guideline and summary of past research. Journal of Operations Management, 30(6), 467–480. https://doi.org/10.1016/j.jom.2012.06.002
   Web of Science ®Google Scholar
• Petker, G., & Petersen, N. (2017). Teacher education students’ struggles with group work in service learning. South African Journal of Childhood Education, 7(1), 8. https://doi.org/10.4102/sajce.v7i1.479
   Google Scholar
• Pitsoe, V. J., & Isingoma, P. (2014). How do school management teams experience teamwork: A case study in the schools in the Kamwenge district, Uganda. Mediterranean Journal of Social Sciences, 5(3), 138. https://doi.org/10.5901/mjss.2014.v5n3p138
   Google Scholar
• Pobiner, B. (2016). Accepting, understanding, teaching, and learning (human) evolution: Obstacles and opportunities. American Journal of Physical Anthropology, 159(Suppl 61), S232–S74. https://doi.org/10.1002/ajpa.22910
   PubMedGoogle Scholar
• Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. The Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
   PubMed Web of Science ®Google Scholar
• Ramayah, T., & Ignatius, J. (2005). Impact of perceived usefulness, perceived ease of use and perceived enjoyment on intention to shop online. The ICFAI Journal of Systems Management, 1–16. http://ramayah.com/journalarticlespdf/impactpeu.pdf
   Google Scholar
• Raza, A. (2020). Intelligent tutoring systems and metacognitive learning strategies: A survey. Muhammad ZAYYAD, 47
   Google Scholar
• Roumbanis Viberg, A., Frykedal, K., & Sofkova Hashemi, S. (2020). Teacher educators’ perceptions of their profession in relation to the digitalization of society. Journal of Praxis in Higher Education, 1(1), 87–110. https://doi.org/10.47989/kpdc80
   Google Scholar
• Sadaf, A., Newby, T. J., & Ertmer, P. A. (2012). Exploring factors that predict preservice teachers’ intentions to use Web 20 technologies using decomposed theory of planned behavior. Journal of Research on Technology in Education, 45(2), 171–196. https://doi.org/10.1080/15391523.2012.10782602
   Google Scholar
• Sakiyo, J., & Badau, K. M. (2015). Assessment of the trend of secondary school students’ academic performance in the sciences, Mathematics and English: Implications for the attainment of the Millennium Development Goals in Nigeria. Advances in Social Sciences Research Journal, 2(2), 31–38. https://doi.org/10.14738/assrj.22.805
   Google Scholar
• Salman, M., & Güven, T. (2021). The evolution usefulness scale: Development, reliability, and validity. Kastamonu Eğitim Dergisi, 29(3), 778–788. https://doi.org/10.24106/kefdergi.933194
   Google Scholar
• Samuel, N., Onasanya, S. A., & Olumorin, C. O. (2018). Perceived usefulness, ease of use and adequacy of use of mobile technologies by Nigerian university lecturers. International Journal of Education and Development Using Information and Communication Technology, 14(3), 5–16.
   Google Scholar
• Sánchez-Prieto, J. C., Cruz-Benito, J., Therón Sánchez, R., & García Peñalvo, F. J. (2020). Assessed by Machines: Development of a TAM-Based Tool to Measure AI-based Assessment Acceptance among Students. International Journal of Interactive Multimedia and Artificial Intelligence, 6(4), 80. https://doi.org/10.9781/ijimai.2020.11.009
   Web of Science ®Google Scholar
• Sánchez‐Prieto, J. C., Huang, F., Olmos‐Migueláñez, S., García‐Peñalvo, F. J., & Teo, T. (2019). Exploring the unknown: The effect of resistance to change and attachment on mobile adoption among secondary pre‐service teachers. British Journal of Educational Technology, 50(5), 2433–2449. https://doi.org/10.1111/bjet.12822
   Web of Science ®Google Scholar
• Sánchez-Prieto, J. C., Olmos-Migueláñez, S., & García-Peñalvo, F. J. (2017). MLearning and pre-service teachers: An assessment of the behavioral intention using an expanded TAM model. Computers in Human Behavior, 72, 644–654. https://doi.org/10.1016/j.chb.2016.09.061
   Web of Science ®Google Scholar
• Sanders, M., & Ngxola, N. (2009). Identifying teachers’ concerns about teaching evolution. Journal of Biological Education, 43(3), 121–128. https://doi.org/10.1080/00219266.2009.9656166
   Web of Science ®Google Scholar
• Sanusi, I. T., Ayanwale, M. A., & Chiu, T. K. F. (2023b). Investigating the moderating effects of social good and confidence on teachers’ intention to prepare school students for artificial intelligence education. Education and Information Technologies, 29, 273–295. https://doi.org/10.1007/s10639-023-12250-1
   Google Scholar
• Sanusi, I. T., Olaleye, S. A., Agbo, F. J., & Chiu, T. K. (2022). The role of learners’ competencies in artificial intelligence education. Computers and Education: Artificial Intelligence, 3, 100098. https://doi.org/10.1016/j.caeai.2022.100098
   Google Scholar
• Sanusi, I. T., Oyelere, S. S., Vartiainen, H., Suhonen, J., & Tukiainen, M. (2023a). A systematic review of teaching and learning machine learning in K-12 education. Education and Information Technologies, 28(5), 5967–5997. https://doi.org/10.1007/s10639-022-11416-7
   Web of Science ®Google Scholar
• Siani, M., & Yarden, A. (2022). I think that teachers do not teach evolution because it is complicated: Difficulties in teaching and learning evolution in Israel. International Journal of Science and Mathematics Education, 20(3), 481–501. https://doi.org/10.1007/s10763-021-10179-w
   Web of Science ®Google Scholar
• Sickel, A. J., & Friedrichsen, P. (2013). Examining the evolution education literature with a focus on teachers: Major findings, goals for teacher preparation, and directions for future research. Evolution: Education and Outreach, 6(1), 1–15. https://doi.org/10.1186/1936-6434-6-23
   Google Scholar
• Smith, M. U. (2010). Current status of research in teaching and learning evolution: II. Pedagogical issues. Science & Education, 19(6-8), 539–571. https://doi.org/10.1007/s11191-009-9216-4
   Web of Science ®Google Scholar
• Song, P., & Wang, X. (2020). A bibliometric analysis of worldwide educational artificial intelligence research development in recent twenty years. Asia Pacific Education Review, 21(3), 473–486. https://doi.org/10.1007/s12564-020-09640-2
   Web of Science ®Google Scholar
• Starbek, P., Starčič Erjavec, M., & Peklaj, C. (2010). Teaching genetics with multimedia results in better acquisition of knowledge and improvement in comprehension. Journal of Computer Assisted Learning, 26(3), 214–224. https://doi.org/10.1111/j.1365-2729.2009.00344.x
   Web of Science ®Google Scholar
• Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society: Series B (Methodological), 36(2), 111–133. https://doi.org/10.1111/j.2517-6161.1974.tb00994.x
   Web of Science ®Google Scholar
• Suárez-Rodríguez, J., Almerich, G., Orellana, N., & Díaz-García, I. (2018). A basic model of integration of ICT by teachers: Competence and use. Educational Technology Research and Development, 66(5), 1165–1187. https://doi.org/10.1007/s11423-018-9591-0
   Web of Science ®Google Scholar
• Sun, L., Zhou, X., & Sun, Z. (2019). Improving cycling behaviors of DocklessBike-sharing users based on an extended theory of planned behavior and credit-based supervision policies in China. Frontiers in Psychology, 10, 2189. https://doi.org/10.3389/fpsyg.2019.02189
   PubMed Web of Science ®Google Scholar
• Sungur-Gül, K., & Ateş, H. (2021). Understanding pre-service teachers’ mobile learning readiness using theory of planned behavior. Educational Technology & Society, 24(2), 44–57.
   Web of Science ®Google Scholar
• Tekkaya, C., Akyol, G., & Sungur, S. (2012). Relationships among teachers’ knowledge and beliefs regarding the teaching of evolution: A case for Turkey. Evolution: Education and Outreach, 5(3), 477–493. https://doi.org/10.1007/s12052-012-0433-y
   Google Scholar
• Terzis, V., & Economides, A. A. (2011). Computer based assessment: Gender differences in perceptions and acceptance. Computers in Human Behavior, 27(6), 2108–2122. https://doi.org/10.1016/j.chb.2011.06.005
   Web of Science ®Google Scholar
• Torcello, L. (2016). The ethics of belief, cognition, and climate change pseudoskepticism: Implications for public discourse. Topics in Cognitive Science, 8(1), 19–48. https://doi.org/10.1111/tops.12179
   PubMed Web of Science ®Google Scholar
• Tran, H. T. T., Nguyen, N. T., & Tang, T. T. (2023). Influences of subjective norms on teachers’ intention to use social media in working. Contemporary Educational Technology, 15(1), ep400. https://doi.org/10.30935/cedtech/12659
   Google Scholar
• Trust, T., Krutka, D. G., & Carpenter, J. P. (2016). Together we are better”: Professional learning networks for teachers. Computers & Education, 102, 15–34. https://doi.org/10.1016/j.compedu.2016.06.007
   Web of Science ®Google Scholar
• Tschannen-Moran, M., Ho, y A. W., & Hoy, W. K. (1998). Teacher efficacy: Its meaning and measure. Review of Educational Research, 68(2), 202–248. https://doi.org/10.3102/00346543068002202
   Web of Science ®Google Scholar
• Tuomi, I. (2019). The impact of artificial intelligence on learning, teaching, and education: Policies for the future. JRC Science for Policy Report. Luxembourg: European Commission.
   Google Scholar
• Turan, Z., Küçük, S., & Karabey, S. (2022). Investigating pre-service teachers’ behavioral intentions to use web 2.0 gamification tools. Participatory Educational Research, 9(4), 172–189. https://doi.org/10.17275/per.22.85.9.4
   Google Scholar
• Venkatesh, V., & Morris, M. (2000). Why don’t men even stop to ask for directions’ gender, social influences and their role in technology acceptance and usage behaviour. MIS Quarterly, 24(1), 115–139. https://doi.org/10.2307/3250981
   Web of Science ®Google Scholar
• Venkatesh, V., Morris, M., Davis, G. & Davis, F. (2003a). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27 (3), 425–478. https://doi.org/10.2307/30036540
   Web of Science ®Google Scholar
• Venkatesh, V., Speier, C., & Morris, M. (2003b). User acceptance enablers in individual decision making about technology: Toward an integrated model. Decision Sciences, 33(2), 297–316. https://doi.org/10.1111/j.1540-5915.2002.tb01646.x
   Web of Science ®Google Scholar
• Violato, C., Marini, A., & Hunter, W. (1989). A confirmatory analysis of a four-factor model of attitudes toward computers: A study of pre-service teachers. Journal of Research on Computing in Education, 22(2), 199–213. https://doi.org/10.1080/08886504.1989.10781915
   Google Scholar
• Walker, A. S. (2019). Perusall: Harnessing AI robo-tools and writing analytics to improve student learning and increase instructor efficiency. The Journal of Writing Analytics, 3(1), 227–263. https://doi.org/10.37514/JWA-J.2019.3.1.11
   Google Scholar
• Wang, Y., Liu, C., & Tu, Y.-F. (2021). Factors affecting the adoption of AI-based applications in higher education: An analysis of teachers’ perspectives using structural equation modelling. Educational Technology & Society, 24(3), 116–129. 10.2307/27032860
   Web of Science ®Google Scholar
• Wang, Y.-S., Wu, M.-C., & Wang, H.-Y. (2009). Investigating the determinants and age and gender differences in the acceptance of mobile learning. British Journal of Educational Technology, 40(1), 92–118. https://doi.org/10.1111/j.1467-8535.2007.00809.x
   Web of Science ®Google Scholar
• Williams, L. J., & McGonagle, A. K. (2016). Detection and remediation of common method variance in PLS analysis. Information Systems Journal, 26(6), 435–466.
   Google Scholar
• World Health Organization. (2018, March 5). Genes and human disease. http://www.who.int/genomics/public/geneticdiseases/en/index2.html.
   Google Scholar
• Wozney, L., Venkatesh, V., & Abrami, P. (2006). Implementing computer technologies: Teachers’ perceptions and practices. Journal of Technology and Teacher Education, 14(1), 173–207.
   Google Scholar
• Yang, S. J. (2021). Guest editorial: Precision education-a new challenge for AI in education. Educational Technology & Society, 24, 105–108.
   Web of Science ®Google Scholar
• Yuen, A. H. K., & Ma, W. W. K. (2008). Exploring teacher acceptance of e-learning technology. Asia-Pacific Journal of Teacher Education, 36(3), 229–243. https://doi.org/10.1080/13598660802232779
   Web of Science ®Google Scholar
• Zhai, X., Haudek, K. C., Shi, L., Nehm, R. H., & Urban-Lurain, M. (2020). From substitution to redefinition: A framework of machine learning-based science assessment. Journal of Research in Science Teaching, 57(9), 1430–1459. https://doi.org/10.1002/tea.21658
   Web of Science ®Google Scholar