PETRI NOKELAINEN Modeling of Professional Growth and Learning Bayesian approach ACADEMIC DISSERTATION To be presented, with the permission of the Faculty of Education of the University of Tampere, for public discussion in the Auditorium of Research Centre for Vocational Education, Korkeakoulunkatu 6, Hämeenlinna, on June 17th, 2008, at 12 o’clock. UNIVERSITY OF TAMPERE ACADEMIC DISSERTATION University of Tampere Department of Education Finland Distribution Tel. +358 3 3551 6055 Bookshop TAJU Fax +358 3 3551 7685 P.O. Box 617 [email protected] 33014 University of Tampere www.uta.fi/taju Finland http://granum.uta.fi Cover design by Juha Siro Acta Universitatis Tamperensis 1317 Acta Electronica Universitatis Tamperensis 728 ISBN 978-951-44-7327-2 (print) ISBN 978-951-44-7328-9 (pdf) ISSN 1455-1616 ISSN 1456-954X http://acta.uta.fi Tampereen Yliopistopaino Oy – Juvenes Print Tampere 2008 For in much wisdom is much grief: and he that increaseth knowledge increaseth sorrow. (Ecclesiastes 1:18) PREFACE First, I would like to acknowledge the examiners of this article dissertation, Professor Erno Lehtinen (University of Turku) and Professor Paul Ilsley (Northern Illinois University). Their comments helped to improve and clarify this work. Next, I wish to give credits to three Professors who have been mentors, co- authors and colleagues for my whole academic career, that is, well over ten years: Pekka Ruohotie (University of Tampere), Henry Tirri (Nokia Research Center) and Kirsi Tirri (University of Helsinki). Pekka has provided me with challenging working opportunities in numerous vocational education research projects since 1995 in my first academic home, the Research Centre for Vocational Education (RCVE, University of Tampere). He has always given me true collegial support and a great amount of academic freedom by allowing me to do distance work. I thank him for being the co-author in the first, third and fourth original research publication. I have had a great pleasure to work with Henry in the field of applied statistics and educational technology applications. I wish to thank him for being a co-author in the fourth original publication and a supportive colleague in my second academic home, the Complex Systems Computation Group (CoSCo, University of Helsinki), while I was working there as a visiting researcher in 1997 – 2005. I wish to express my gratitude to Kirsi as a co-author of the second original publication, mentor and colleague with whom I have been successfully working in the research field of gifted and religious education since 1996. She has always been a supportive scholar and given me many opportunities to reach for the higher research standards. My special thanks goes to a computer scientist and statistician M.Sc. Tomi Silander (CoSCo) not only for being my co-author in the fourth original publication, but also for his valuable feedback on too-many-to-mention-here statistical issues (especially in section 4.4 in this dissertation) and his incredible programming skills. I also wish to thank my other colleagues during the CoSCo years: Ph.D. Jaakko Kurhila, M.Sc. Jussi Lahtinen and M.Sc. Miikka Miettinen. Miikka and Jaakko have joined their efforts with me to design an online questionnaire that I used in data collection for many years. Jussi has given me valuable comments on data visualization techniques and helped me with the data analysis. I wish to thank Ph.D. Hanna-Leena Merenti-Välimäki (EVTEK polytechnic institution of higher education) as a co-author in the second original publication. Ph.D. Seppo Kolehmainen (HAMK polytechnic institution of higher education) has given valuable comments regarding the Growth-oriented Atmosphere Questionnaire. Working with Professor Erkki Komulainen (University of Helsinki) improved the quality of the third original publication considerably. I am also grateful to Ph.D. Jorma Saarinen (HAMK polytechnic institution of higher education) and Professor Hannele Niemi (University of Helsinki) for our past projects that helped me to test my models with empirical samples. Ph.D. Bruce Beairsto has been proof-reading all of the original publications and he has given some valuable comments. I want to thank all the researchers and staff at the RCVE. My special thanks goes to Airi, Hilkka, Jaana, Kaija, Lea, Mika and Tarja for tolerating my flexible timing, unpredictable administrative standards and helping me with many practical matters for all these years. My research work with all these aforementioned scholars has been funded by aforementioned universities together with numerous funding partners (e.g., European Union, Finnish Academy, Finnish Work Environment Fund, Ministry of Education, Tekes). I would also like to thank Ella and Georg Ehrnrooth foundation for a grant that helped me to complete this work. Finally, thank You, God Almighty, for giving me Raila and two lovely children, Seela and Ruut. Tuulos, April 2008 Petri Nokelainen ABSTRACT The major goal of the study was to contribute both to the basic research on professional growth and learning, and to the development and use of quantitative research methodology in these research areas. The research goal was addressed with four empirical studies conducted between 2002-2007. The research questions were as follows: 1) What is the optimal number of dimensions and items in the Growth-oriented Atmosphere Questionnaire (GOAQ) to describe the theoretical model of growth-oriented atmosphere? (Study I); 2) Are the theoretical dimensions of the Self-confidence attitude attribute Scales (SaaS) questionnaire identified in the domain of three groups of mathematically gifted participants: Academic mathematics Olympians, polytechnic institute of higher education students, and elementary school students who have participated in mathematical competitions? (Study II); 3) What is the optimal number of dimensions and items in the Abilities for Professional Learning Questionnaire (APLQ) to describe the theoretical model of learning experiences and motivation? (Study III); 4) Is there a difference between substantive interpretations of the results of Bayesian dependency modeling and linear bivariate correlational analysis with professional growth data that has both linear and non-linear dependencies? (Study IV); 5) Is there a difference between substantive interpretations of the results of Bayesian dependency modeling and linear confirmatory factor analysis with professional growth data that has both linear and non-linear dependencies? (Study IV). Results of the first study showed that the theoretical four group classification of the growth-oriented atmosphere factors was supported by the empirical evidence: 1) Support and rewards from the management; 2) Incentive value of the job; 3) Operational capacity of the team; 4) Work related stress. Further, the results of categorical factor analysis showed that the 67-item and thirteen-factor solution was the most interpretable in terms of correspondence to the theoretical GOA model. Results of the second study showed that the theoretical four group classification of the self-confidence attitude attribute factors was supported by the empirical evidence: 1) Success due to ability; 2) Failure due to a lack of ability; 3) Success due to effort; 4) Failure due to a lack of effort. Further, the results of exploratory factor analysis showed that the eight item and four factor solution was the most interpretable in terms of the attribution theory. Results of the third study showed that the theoretical six group classification of the motivational factors was supported by the empirical evidence: 1) Intrinsic goal orientation; 2) Extrinsic goal orientation; 3) Meaningfulness of study; 4) Control beliefs; 5) Self-efficacy; 6) Test anxiety. Further, the results of confirmatory factor analysis showed that the 21-item solution was the most interpretable in terms of correspondence to the baseline model. Results of the fourth study showed that in general Bayesian network models were congruent with the correlation matrixes as both techniques found the same variables independent of all the other variables. However, non-linear modeling found with both linear and non-linear samples a greater number of strong dependencies between the GOA factors. Results further showed that by using linear methods with non-linear data may lead to different substantive interpretations when using non-linear methods with the same data. Keywords: Professional growth and learning, organizational atmosphere, learning motivation, self-attributions, quantitative research methods, Bayesian modeling TIIVISTELMÄ Tutkimus keskittyi ammatillisen kasvun ja oppimisen tutkimusongelmien mallintamiseen bayesilaisten analyysimenetelmien avulla. Tutkimustehtävä toteutettiin vuosien 2002 – 2007 aikana suoritetuilla neljällä empiirisellä tutkimuksella. Tutkimuskysymykset olivat seuraavat: 1) Mikä on kasvuorientaatiomittarin (Growth-oriented Atmosphere Questionnaire, GOAQ) kasvuorientoituneen ilmapiirin teoreettisen mallin kuvauksen kannalta optimaalinen ulottuvuuksien ja väittämien lukumäärä? (Tutkimus I); 2) Ovatko itseluottamusta ja attribuutioita mittaavan Self-confidence attitude attribute Scales (SaaS) –kyselyn teoreettiset ulottuvuudet tunnistettavissa kolmessa matemaattisesti lahjakkaista ihmisistä (matematiikan olympistit, teknillisen ammattikorkeakoulun matematiikkalinjalaiset, peruskoulun ja lukion matematiikkakilpailun osallistujat) koostuvassa ryhmässä? (Tutkimus II); 3) Mikä on ammatillisen oppimisen valmiuksiin keskittyvän mittarin (Abilities for Professional