Relationship between Attitude to Career and Technology Course with Technological Attitude of High School Students in Tabriz

Subject Areas : Educational Psychology

Maryam Hosseinzadeh Nabati ¹ (Tabriz)
Firooz Mahmoodi ² (education department, University of Tabriz, Tabriz, East Azarbayjan)
Yousef Adib ³ (Tabriz)

Received: 2020-01-19 Accepted : 2020-06-23 Published : 2020-05-21

Keywords: Technology learning self-regulation, Technology Learning, Career and Technology Course, Attitude toward Technology learning,

Abstract :

The main purpose of this study was to determine the relationship between attitude to career and technology course and technological attitude. This research is a quantitative, an applied in terms of purpose, and a correlation-descriptive study. The population consisted of all 15386 high school students in district 1 of education organization at Tabriz in 2018-2019. The 415 students selected as sample by random stratified sampling. Data were collected from 415 secondary education students through Technology Attitude Questionnaire (Lioua and Kuo, 2014) and a researcher-made questionnaire to assess attitudes toward career and technology course and were analyzed using a two-step Structural Equation Model (SEM) approach with Smart Pls 3.2.8. After testing the adequacy of the measurement model, the structural model showed that attitudes to career and technology course significantly predicted technological attitude, such as Technology Learning self-efficacy(0.44), Technology learning value (0.56), Technology learning strategies (0.56), Technology Learning goal orientation (.63), environment stimulation (0.64), Technology Learning self-regulation triggering (0.54), Technology Learning self-regulation-implementing (0.68).these results shed light on the critical role of career and technology course for students’ technological attitude. Due to these relationships, it is suggested that the curriculum of work and technology be given more attention and that its objects, content, and teaching-learning strategies be prepared and implemented in a practical style.

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Cotgreave, P., & Davies, R. (2005). How can we measure the success of national science policies in the short or medium terms? European Journal of Education, 40(4), 393-403.

Cronbach, L. J. (1951). Coefficient Alpha and the Internal Structure of Tests", Psychometrika. 16(3), 297-334.

Cronbach, L. J. (2004). My current thoughts on coefficient alpha and successor procedures. Educational and Psychological Measurement. 64(3), 391–418. doi.org/10.1177/0013164404266386

Davari, Ali; Rezazadeh, Arash. (2016). "Modeling Structural Equations with PLS Software" Academic Jihad Publications, Third Edition. [In Persian].

De Vries, M. J. (2016). Teaching about technology: An introduction to the philosophy of technology for non-philosophers. Springer.

Dijkstra, T. K., and Henseler, J. (2015).  Consistent and Asymptotically Normal PLS Estimators for Linear Structural Equations.Computational Statistics & Data Analysis. 81(1) 10-23.

Dobson, R., & Burke, K. (2013). Spotlight on science learning: The high cost of dropping science and math. Let's Talk Science: Inspiring Discovery.

Duncan, T. G., & McKeachie, W. J. (2005). The making of the motivated strategies for learning questionnaire. Educational psychologist, 40(2), 117-128.

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Fornell, C. and Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research. 18(1), 39–50. 

Foster, E. (2010). A New Equation: How Encore Careers in Math and Science Education Equal More Success for Students. National Commission on Teaching and America's Future.

Griffin, L. (2015). How Do We Improve Elementary Math Education? It Starts in
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Haas, J. (2005). The situation in industry and the loss of interest in science education. European Journal of Education, 40(4), 405-416.

Hansenne, M., & Bianchi, J. (2009). Emotional intelligence and personality in major depression: trait versus state effects. Psychiatry Research, 166(1), 63-68.

Hasni, A., & Potvin, P. (2015). Student's Interest in Science and Technology and Its Relationships with Teaching Methods, Family Context and Self-Efficacy. International Journal of Environmental and Science Education, 10(3), 337-366.

Henseler, Jörg & Ringle, Christian & Sinkovics, Rudolf. (2009). The Use of Partial Least Squares Path Modeling in International Marketing. Publisher: Emerald JAI Press. 277-319.

Hooi, T. K., Abu, N. H. B., & Rahim, M. K. I. A. (2018). Relationship of big data analytics capability and product innovation performance using smartPLS 3.2. 6: Hierarchical component modelling in PLS-SEM. International Journal of Supply Chain Management, 7(1), 51-64.

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Kinzie, M. B., Delcourt, M. A., & Powers, S. M. (1994). Computer technologies: Attitudes and self-efficacy across undergraduate disciplines. Research in higher education, 35(6), 745-768.

Krejcie, R. V., & Morgan, D. W. (1970). Determining sample size for research activities. Educational and psychological measurement, 30(3), 607-610.

Liou, P. Y., & Kuo, P. J. (2014). Validation of an instrument to measure students’ motivation and self-regulation towards technology learning. Research in Science & Technological Education, 32(2), 79-96.

Lynch, D. J., & Trujillo, H. (2011). Motivational beliefs and learning strategies in organic chemistry. International Journal of Science and Mathematics Education, 9(6), 1351-1365.

Mawson, B. (2010). Children’s developing understanding of technology. International Journal of Technology and Design Education, 20(1), 1-12.

Mayya, S., & Roff, S. (2004). Students′ perceptions of educational environment: a comparison of academic achievers and under-achievers at kasturba medical college, India. Education for health, 17(3), 280-291.

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Ministry of Education of the Islamic Republic of Iran (2012). National Curriculum Document. Ministry of Education [In Persian].

Mooij, T. (2007). Learning for self-regulation. Improving instructional benefits for pupils, teachers, schools and society at large. Heerlen: Open University of the Netherlands.

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Osborne, J., & Collins, J. (2001). Pupils’ and Parents’ Views of the School Science Curriculum. London: King’s College London.

Pajares, F., Britner, S. L., & Valiante, G. (2000). Relation between achievement goals and self-beliefs of middle school students in writing and science. Contemporary educational psychology, 25(4), 406-422.

Pintrich, P. R., Marx, R. W., & Boyle, R. A. (1993). Beyond cold conceptual change: The role of motivational beliefs and classroom contextual factors in the process of conceptual change. Review of Educational research, 63(2), 167-199.

Shahitawi, Y. (1394). The role of education and the textbook of career and technology in the flourishing of creativity, innovation and entrepreneurship in students. First International Conference on Entrepreneurship, Creativity and Innovation, Shiraz, Kharazmi Higher Institute of Science and Technology. [In Persian].

Steffens, K. (2006). Self‐regulated learning in technology‐enhanced learning environments: Lessons of a European peer review. European journal of education, 41(3‐4), 353-379.

Stein, S.J. & McRobbie, C. J. (1997). Students' conceptions of science across the years of schooling. Research in Science Education, 27(4), 611–628. doi.org/10.1007/BF02461484

Tosun, C., & Şekerci, A. R. (2015). The Role of Motivation in Self-Regulation Skills in Eighth Grade Students’ Science Classes. Erzincan Üniversitesi Eğitim Fakültesi Dergisi, 17(1), 1-29.

Van den Berghe, W., & MAS, D. M. (2012). Choosing STEM. Young people's educational choice for technical and scientific studies. The Flemish Council for Science and Innovation.

Van Der Bijl-Brouwer, M., & Van Der Voort, M. (2014). Understanding design for dynamic and diverse use situations. International Journal of Design, 8(2), 29-42.

Van Petegem, K., Aelterman, A., Van Keer, H., & Rosseel, Y. (2008). The influence of student characteristics and interpersonal teacher behaviour in the classroom on student’s wellbeing. Social Indicators Research, 85(2), 279-291.

Velayutham, S., Aldridge, J. M., & Fraser, B. (2012). Gender differences in student motivation and self-regulation in science learning: A multi-group structural equation modeling analysis. International journal of science and mathematics education, 10(6), 1347-1368.

Vinzi, V. E., Trinchera, L., & Amato, S. (2010). PLS path modeling: from foundations to recent developments and open issues for model assessment and improvement. In Handbook of partial least squares (pp. 47-82). Springer, Berlin, Heidelberg.

Steffens, K. (2006). Self‐Regulated Learning in Technology‐Enhanced Learning environments: lessons of a European peer review. European Journal of Education, 41, (3/4), 353-379.

     https://doi.org/10.1111/j.1465-3435.2006.00271.x

Wigfield, A., Eccles, J. S., & Pintrich, P. R. (1996). Development between the ages of ll and 25. In D. C. Berliner & R. C. Calfee (Eds.), Handbook of educational psychology (pp. 148–185). New York: Routledge.

Yousefi Saaid Abadi, R. (2008). Developing a Paper Conceptual Framework for Institutionalization ICT in Primary Education. Journal of Instruction and Evaluation, 1(1), 199-226. [In Persian].

Zimmerman, B. J. (2000). Self-efficacy: An essential motive to learn. Contemporary educational psychology, 25(1), 82-91.

Zimmerman, B. J., & Schunk, D. H. (2011). Handbook of self-regulation of learning and performance. Routledge/Taylor & Francis Group

Adib, Y., Ezzati, M., Fathiazar, E., & Mahmudi, F. (2016). Evaluation of Attention to Technology Literacy Level in General Education (grades 1-9). Journal of Instruction and Evaluation, 9(35), 125-154 [In Persian].

Al-Harthy, I. S., Was, C. A., & Isaacson, R. M. (2010). Goals, efficacy and metacognitive self-regulation a path analysis. International Journal of Education, 2(1), 1-20. doi.org/10.5296/ije.v2i1.357

Allcott, H. (2011). Social norms and energy conservation. Journal of public Economics, 95(9-10), 1082-1095. doi.org/10.1016/j.jpubeco.2011.03.003

Bartolomé, A., & Steffens, K. (2011). Technologies for self-regulated learning. In Self-regulated learning in technology enhanced learning environments (pp. 21-31). Brill Sense.

Beishuizen, J., Carneiro, R., & Steffens, K. (2007). Self-regulated learning in technology enhanced learning environments: Individual learning and communities of learners. In Proceedings of the KALEIDOSCOPE-TACONET conference in Amsterdam.

Bong, M. (2004). Academic motivation in self-efficacy, task value, achievement goal orientations, and attributional beliefs. The Journal of Educational Research, 97(6), 287-298.

Carneiro, R., Lefrere, P., Steffens, K., & Underwood, J. (Eds.). (2012). Self-regulated learning in technology enhanced learning environments, (Vol. 5). Springer Science & Business Media.

Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern methods for business research, 295(2), 295-336.

Chin, W. W., Marcolin, B. L., & Newsted, P. R.(2003).  "A Partial Least Squares Latent Variable Modeling Approach for Measuring Interaction Effects: Results from a Monte Carlo Simulation Study and an Electronic-Mail Emotion/Adoption Study". Information Systems Research. 14(2), 189-217.

Convert, B. (2005). Europe and the crisis in scientific vocations. European Journal of Education, 40(4), 361-366.

Cotgreave, P., & Davies, R. (2005). How can we measure the success of national science policies in the short or medium terms? European Journal of Education, 40(4), 393-403.

Cronbach, L. J. (1951). Coefficient Alpha and the Internal Structure of Tests", Psychometrika. 16(3), 297-334.

Cronbach, L. J. (2004). My current thoughts on coefficient alpha and successor procedures. Educational and Psychological Measurement. 64(3), 391–418. doi.org/10.1177/0013164404266386

Davari, Ali; Rezazadeh, Arash. (2016). "Modeling Structural Equations with PLS Software" Academic Jihad Publications, Third Edition. [In Persian].

De Vries, M. J. (2016). Teaching about technology: An introduction to the philosophy of technology for non-philosophers. Springer.

Dijkstra, T. K., and Henseler, J. (2015).  Consistent and Asymptotically Normal PLS Estimators for Linear Structural Equations.Computational Statistics & Data Analysis. 81(1) 10-23.

Dobson, R., & Burke, K. (2013). Spotlight on science learning: The high cost of dropping science and math. Let's Talk Science: Inspiring Discovery.

Duncan, T. G., & McKeachie, W. J. (2005). The making of the motivated strategies for learning questionnaire. Educational psychologist, 40(2), 117-128.

Educational Research and Planning Organization (2019). Seventh work and technology. Tehran: Office of General and Secondary Textbooks. [In Persian].

Fornell, C. and Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research. 18(1), 39–50. 

Foster, E. (2010). A New Equation: How Encore Careers in Math and Science Education Equal More Success for Students. National Commission on Teaching and America's Future.

Griffin, L. (2015). How Do We Improve Elementary Math Education? It Starts in
School. Technology.

Haas, J. (2005). The situation in industry and the loss of interest in science education. European Journal of Education, 40(4), 405-416.

Hansenne, M., & Bianchi, J. (2009). Emotional intelligence and personality in major depression: trait versus state effects. Psychiatry Research, 166(1), 63-68.

Hasni, A., & Potvin, P. (2015). Student's Interest in Science and Technology and Its Relationships with Teaching Methods, Family Context and Self-Efficacy. International Journal of Environmental and Science Education, 10(3), 337-366.

Henseler, Jörg & Ringle, Christian & Sinkovics, Rudolf. (2009). The Use of Partial Least Squares Path Modeling in International Marketing. Publisher: Emerald JAI Press. 277-319.

Hooi, T. K., Abu, N. H. B., & Rahim, M. K. I. A. (2018). Relationship of big data analytics capability and product innovation performance using smartPLS 3.2. 6: Hierarchical component modelling in PLS-SEM. International Journal of Supply Chain Management, 7(1), 51-64.

Hulleman, C. S., Schrager, S. M., Bodmann, S. M., & Harackiewicz, J. M. (2010). A meta-analytic review of achievement goal measures: Different labels for the same constructs or different constructs with similar labels? Psychological bulletin, 136(3), 422-449.

Johnson, P. (2018). Fundamentals of collection development and management. American Library Association.

Kareshki, H., Arfaa, F., & Shirzad, Z. (2014). Relationship between environmental perceptions of students with their academic attitudes. New Educational Approaches, 9(1), 75-88. .  [In Persian].

Kee, K. S., Horan, W. P., Salovey, P., Kern, R. S., Sergi, M. J., Fiske, A. P.,... & Green, M. F. (2009). Emotional intelligence in schizophrenia. Schizophrenia Research, 107(1), 61-68.

Kinzie, M. B., Delcourt, M. A., & Powers, S. M. (1994). Computer technologies: Attitudes and self-efficacy across undergraduate disciplines. Research in higher education, 35(6), 745-768.

Krejcie, R. V., & Morgan, D. W. (1970). Determining sample size for research activities. Educational and psychological measurement, 30(3), 607-610.

Liou, P. Y., & Kuo, P. J. (2014). Validation of an instrument to measure students’ motivation and self-regulation towards technology learning. Research in Science & Technological Education, 32(2), 79-96.

Lynch, D. J., & Trujillo, H. (2011). Motivational beliefs and learning strategies in organic chemistry. International Journal of Science and Mathematics Education, 9(6), 1351-1365.

Mawson, B. (2010). Children’s developing understanding of technology. International Journal of Technology and Design Education, 20(1), 1-12.

Mayya, S., & Roff, S. (2004). Students′ perceptions of educational environment: a comparison of academic achievers and under-achievers at kasturba medical college, India. Education for health, 17(3), 280-291.

MehrMohammadi, M. (2012). Recognition of basic life skills in the age of information and communication technology. Quarterly Journal of Education, 28(3); 21-44.  [In Persian].

Ministry of Education of the Islamic Republic of Iran (2012). National Curriculum Document. Ministry of Education [In Persian].

Mooij, T. (2007). Learning for self-regulation. Improving instructional benefits for pupils, teachers, schools and society at large. Heerlen: Open University of the Netherlands.

Nota, L., Soresi, S., & Zimmerman, B. J. (2004). Self-regulation and academic achievement and resilience: A longitudinal study. International journal of educational research, 41(3), 198-215.

Nunnally, J.C. (1978). Psychometric theory. 2nd Edition, McGraw-Hill, New York.

OECD (Organisation for Economic Co-operation and Development). (2006). Assessing Scientific, Reading and Mathematical Literacy: A Framework for PISA. Paris: Organisation for Economic Co-operation and Development.

Osborne, J., & Collins, J. (2001). Pupils’ and Parents’ Views of the School Science Curriculum. London: King’s College London.

Pajares, F., Britner, S. L., & Valiante, G. (2000). Relation between achievement goals and self-beliefs of middle school students in writing and science. Contemporary educational psychology, 25(4), 406-422.

Pintrich, P. R., Marx, R. W., & Boyle, R. A. (1993). Beyond cold conceptual change: The role of motivational beliefs and classroom contextual factors in the process of conceptual change. Review of Educational research, 63(2), 167-199.

Shahitawi, Y. (1394). The role of education and the textbook of career and technology in the flourishing of creativity, innovation and entrepreneurship in students. First International Conference on Entrepreneurship, Creativity and Innovation, Shiraz, Kharazmi Higher Institute of Science and Technology. [In Persian].

Steffens, K. (2006). Self‐regulated learning in technology‐enhanced learning environments: Lessons of a European peer review. European journal of education, 41(3‐4), 353-379.

Stein, S.J. & McRobbie, C. J. (1997). Students' conceptions of science across the years of schooling. Research in Science Education, 27(4), 611–628. doi.org/10.1007/BF02461484

Tosun, C., & Şekerci, A. R. (2015). The Role of Motivation in Self-Regulation Skills in Eighth Grade Students’ Science Classes. Erzincan Üniversitesi Eğitim Fakültesi Dergisi, 17(1), 1-29.

Van den Berghe, W., & MAS, D. M. (2012). Choosing STEM. Young people's educational choice for technical and scientific studies. The Flemish Council for Science and Innovation.

Van Der Bijl-Brouwer, M., & Van Der Voort, M. (2014). Understanding design for dynamic and diverse use situations. International Journal of Design, 8(2), 29-42.

Van Petegem, K., Aelterman, A., Van Keer, H., & Rosseel, Y. (2008). The influence of student characteristics and interpersonal teacher behaviour in the classroom on student’s wellbeing. Social Indicators Research, 85(2), 279-291.

Velayutham, S., Aldridge, J. M., & Fraser, B. (2012). Gender differences in student motivation and self-regulation in science learning: A multi-group structural equation modeling analysis. International journal of science and mathematics education, 10(6), 1347-1368.

Vinzi, V. E., Trinchera, L., & Amato, S. (2010). PLS path modeling: from foundations to recent developments and open issues for model assessment and improvement. In Handbook of partial least squares (pp. 47-82). Springer, Berlin, Heidelberg.

Steffens, K. (2006). Self‐Regulated Learning in Technology‐Enhanced Learning environments: lessons of a European peer review. European Journal of Education, 41, (3/4), 353-379.

     https://doi.org/10.1111/j.1465-3435.2006.00271.x

Wigfield, A., Eccles, J. S., & Pintrich, P. R. (1996). Development between the ages of ll and 25. In D. C. Berliner & R. C. Calfee (Eds.), Handbook of educational psychology (pp. 148–185). New York: Routledge.

Yousefi Saaid Abadi, R. (2008). Developing a Paper Conceptual Framework for Institutionalization ICT in Primary Education. Journal of Instruction and Evaluation, 1(1), 199-226. [In Persian].

Zimmerman, B. J. (2000). Self-efficacy: An essential motive to learn. Contemporary educational psychology, 25(1), 82-91.

Zimmerman, B. J., & Schunk, D. H. (2011). Handbook of self-regulation of learning and performance. Routledge/Taylor & Francis Group