Research Article
BibTex RIS Cite

KAMU YÖNETİMİ ÖĞRENCİLERİNİN DERS İÇERİK DEĞERLENDİRMELERİNİ ETKİLEYEN FAKTÖRLER

Year 2020, Volume: 12 Issue: 4, 332 - 344, 05.10.2020

Abstract

Bu çalışma, kamu yönetimi öğrencilerinin aldıkları dersin ders içeriğine yönelik değerlendirmelerini etkileyen faktörleri ortaya koymaktadır. Çalışmadaki empirik veriler üç farklı üniversitede eğitim görmekte olan toplam 171 kamu yönetimi bölümü öğrencisinden anket yoluyla toplanmıştır. Keşfedici faktör analizi, Cronbach’s alfa testi verilerin analizinde kullanılmış ve ayrıca değişkenler arası ilişkiler yapısal eşitlik modellemesi yoluyla sınanmıştır. Bu sayede geçerli ve güvenilir bir model ortaya konmuştur. Çalışmada keşfedici faktör analizi ve doğrulayıcı faktör analizi yöntemleri birarada kullanılarak muhtemel ortak yöntem varyans (OYV) problemi en alt seviyeye indirilmiştir. Çalışmada, kamu ve vakıf üniversitelerinde eğitim gören kamu yönetimi öğrencilerinin ders içeriği değerlendirmeleri arasında anlamlı bir fark bulunmuştur. Ayrıca, öğrencilerin tatmini ve algılanan önem de öğrencilerin ders içerik değerlendirmelerini etkilemektedir.

References

  • Alves, H., & Raposo, M. (2009). The measurement of the construct satisfaction in higher education. The Service Industries Journal, 29(2), 203-218.
  • Aoki, N. (2015). Institutionalization of New Public Management: The case of Singapore’s education system. Public Management Review, 17(2), 165-186.
  • Arbaugh, J. B. (2000). Virtual classroom characteristics and student satisfaction with internet-based MBA courses. Journal of Management Education, 24(1), 32-54.
  • Atchley, W., Wingenbach, G. & Akers, C. (2013). Comparison of course completion and student performance through online and traditional courses. The International Review of Research in Open and Distance Learning, 14(4), 104–116.
  • Banerjee, M. & Brinckerhoff, L. C. (2002). Assessing student performance in distance education courses: Implications for testing accommodations for students with learning disabilities. Assessment for Effective Intervention, 27(3), 25-35.
  • Bentler, P.M. (1990). Comparative fit indexes in structural equation models. Psychological Bulletin, 107, 238-246.
  • Betoret, F. D. (2007). The influence of students’ and teachers’ thinking styles on student course satisfaction and on their learning process. Educational Psychology, 27(2), 219-234.
  • Brans, M. & Coenen, L. (2016) The Europeanization of Public Administration teaching, Policy and Society, 35:4, 333-349.
  • Bright, L., & Graham Jr, C. B. (2016). Predictors of graduate student satisfaction in public administration programs. Journal of Public Affairs Education, 22(1), 17-34.
  • Byrne, B.M. (1998). Structural Equation Modeling with LISREL, PRELIS and SIMPLIS. NJ: Lawrence Erlbaum Associates.
  • Buckley, A. (2013). Engagement for enhancement: Report of a UK survey pilot. York: Higher Education Academy.
  • Chamillard, A. T., & Merkle, L. D. (2002). Evolution of an introductory computer science course: The long haul. Journal of Computing Sciences in Colleges, 18(1), 144-153.
  • Chan, A. P., Lam, P. T., Chan, D. W., Cheung, E., & Ke, Y. (2010). Critical success factors for PPPs in infrastructure developments: Chinese perspective. Journal of Construction Engineering and Management, 136(5), 484-494.
  • Colorado, J. T. & Eberle, J. (2010). Student demographics and success in online learning environments. Emporia State Research Studies, 46(1), 4–10.
  • De Carvalho, J., & Chima, F. O. (2014). Applications of structural equation modeling in social sciences research. American International Journal of Contemporary Research, 4(1), 6-11.
  • Durmuş, B., Yurtkoru, E. S., & Çinko, M. (2013). Sosyal Bilimlerde SPSS İle Veri Analizi. [Data Analysis in Social Sciences with SPSS]. (5th edition). İstanbul: Beta Publishing.
  • Ebdon, C. (1999). Teaching public finance administration online: A case study. Journal of Public Affairs Education, 5(3), 237-246.
  • Elliot, K. M., & Healy, M. A. (2001). Key factors influencing student satisfaction related to recruitment and retention. Journal of Marketing for Higher Education, 10, 1-11.
  • Finnie, R., Mueller, R. E., & Childs, S. (2010). Family Background, Cultural Capital and Access to Higher Education in Canada. Graduate School of Public and International Affairs. MESA Project (s. 9-12). Germany: XXIV Annual Conference of the European Society for Population Economics.
  • Fox, P., & Skitmore, M. (2007). Factors facilitating construction industry development. Building Research and Information, 35(2), 178-188.
  • Garg, M. (2018). Student satisfaction as determinant of academic success of distance learners: A study across distance learning courses. The Online Journal of Distance Education and e-Learning, 6(3), 30-43.
  • George, D., & Mallery, P. (2016). IBM SPSS Statistics 23 Step by Step: A Simple Guide and Reference. New York, NY: Routledge.
  • Gürbüz, S. (2019). Amos ile yapısal eşitlik modellemesi. Ankara: Seçkin.
  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). In J. F. Hair, Jr., W. C. Black, B. J. Babin, & R. E. Anderson (Eds.), Multivariate data analysis (Pearson new international seventh ed.). Harlow, Essex: Pearson.
  • Han, H., Kiatkawsin, K., Kim, W., & Hong, J. H. (2018). Physical classroom environment and student satisfaction with courses. Assessment & Evaluation in Higher Education, 43(1), 110-125.
  • Harris, G., Froman, J., & Surles, J. (2009). The professional development of graduate mathematics teaching assistants. International Journal of Mathematical Education in Science and Technology, 40(1), 157-172.
  • Hau-Siu Chow, I. (1995). Management education in Hong Kong: needs and challenges. International Journal of Educational Management, 9(5), 10-15.
  • Hearn, J. C. (1985). Determinants of college students' overall evaluations of their academic programs. Research in Higher Education, 23(4), 413-437.
  • Holtbrügge, D., & Engelhard, F. (2016). Study Abroad Programs: Individual Motivations, Cultural Intelligence, and the Mediating Role of Cultural Boundary Spanning. Academy of Management Learning & Education, 15(3), 435-455.
  • Horvat, A., Krsmanovic, M., & Djuric, M. (2012). Differences in students` satisfaction with distance learning studies. International Journal of Social, Education, Economics and Management Engineering, 6(6), 1412-1415.
  • Ijaz, A., Irfan, S. M., Shahbaz, S., Awan, M., & Sabir, M. (2011). An empirical model of student satisfaction: Case of Pakistani public sector business schools. Journal of quality and Technology Management, 7(2), 91-114.
  • Irani, T. (1998). Communication potential, information richness and attitude: A study of computer mediated communication in the ALN classroom. ALN magazine, 2(1), 1-12.
  • Jakobsen, M., & Jensen, R. (2015). Common method bias in public management studies. International Public Management Journal, 18(1), 3–30.
  • Keller, J. (1983). Motivational design of instruction. In Reigeluth, C. M. (Ed.), Instructional design theories and models: An overview of their current status (1st ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.
  • Kline, P. (1994). An Easy Guide to Factor Analysis. London: Routledge.
  • Kline, R. B. (1998). Principles and Practice of Structural Equation Modeling. New York: The Guilford Press.
  • Liem, G. A. D. (2019). Academic performance and assessment. Educational Psychology, 39(6), 705-708. DOI: 10.1080/01443410.2019.1625522
  • Lopez-Littleton, V. & Blessett, B. (2015). A Framework for Integrating Cultural Competency into the Curriculum of Public Administration Programs, Journal of Public Affairs Education, 21:4, 557-574, DOI: 10.1080/15236803.2015.12002220
  • Medsker, L., & Turban, E. (1994). Integrating expert systems and neural computing for decision support. Expert Systems with Applications, 7(4), 553-562.
  • Min, S., & Mentzer, J. T. (2004). Developing and measuring supply chain management concepts. Journal of business logistics, 25(1), 63-99.
  • Moore, M. G. (1989). Editorial: Three types of interaction, The American Journal of Distance Education. 3(2), 1-6.
  • Moore, M. G., & Kearsley, G. (1996). Distance education: A systems view. New York, NY: Wadsworth.
  • Nu’man, A. (2012). A Framework for Adopting E-Voting in Jordan. Electronic Journal of e-Government, 10(2), 133-146.
  • Özdamar, K. (1999). Paket Programlar İle İstatistiksel Veri Analizi [Statistical data analysis by custom softwares]. Eskişehir: Kaan Publishing.
  • Özdemirci, A., Özcan, E. D., & İldaş, G. (2014). The Relationship between World Views of Rectors with Corporate. International Journal of Business and Management, 9(1), 149-167.
  • Pallant, J. (2001). SPSS survival manual: A step-by-step guide to data analysis using SPSS for windows. Philadelphia, PA: Open University Press.
  • Pike, G. R. (1993). The relationship between perceived learning and satisfaction with college: An alternative view. Research in Higher Education, 34(1), 23-40.
  • Podsakoff, P.M., MacKenzie, S.B., Lee, J.Y. and Podsakoff, N.P. (2003), “Common method biases in behavioral research: a critical review of the literature and recommended remedies”, Journal of Applied Psychology, 88(5), 879-903.
  • Powell, D. C. (2007). Student satisfaction with a distance learning MPA program: A preliminary comparison of on-campus and distance learning students’ satisfaction with MPA courses. MERLOT Journal of Online Learning and Teaching, 3 (1), 1-18.
  • Rissi, J.J. & Gelmon, S.B. (2014). Development, Implementation, and Assessment of a Competency Model for a Graduate Public Affairs Program in Health Administration, Journal of Public Affairs Education, 20:3, 335-352.
  • Romiszowski, A. (2004). How’s the e-learning baby? Factors leading to success or failure of an educational technology innovation. Educational Technology, 44(1), 5–27.
  • Rubaii, N. (2019). Why research methods matter: Essential skills for decision-making. Journal of Public Affairs Education, 25:2, 277-279, DOI: 10.1080/15236803.2019.1565253
  • Sakri, S., Salim, J. & Sembok, T.M.T. (2012). Information Communications and Technology (ICT) Abuse in the Malaysian Public Sector: The Influence of Ethical, Organisational Bond and General Deterrence Factors. Akademika 82(1), 125-137.
  • Santor, D. A., Haggerty, J. L., Lévesque, J. F., Burge, F., Beaulieu, M. D., Gass, D., & Pineault, R. (2011). An overview of confirmatory factor analysis and item response analysis applied to instruments to evaluate primary healthcare. Healthcare Policy, 7(Special Issue), 79 - 92.
  • Schram, C. M. (1996). A meta-analysis of gender differences in applied statistics achievement. Journal of Educational and Behavioral Statistics, 21(1), 55-70.
  • Shea, P. J., Pickett, A. M., & Pelz, W. E. (2003). A follow-up investigation of teaching presence in the SUNY learning network. Journal of Asynchronous Learning Networks, 7(2), 61–80.
  • Shur, D.D. (2006). Exploratory or Confirmatory Factor Analysis? Statistics and Data Analysis, SUGI 31. Retrieved January 11, 2018, from http://tx.liberal.ntu.edu.tw/~PurpleWoo/Literature/!DataAnalysis/FactorAnalysis_SAS.com_200-31.pdf.
  • Simpson, C., & Du, Y. (2004). Effects of learning styles and class participation on students' enjoyment level in distributed learning environments. Journal of Education for Library and Information Science, 123-136.
  • Swan, K., Shea, P., Fredericksen, E., Pickett, A., Pelz, W., & Maher, G. (2000). Building knowledge building communities: Consistencies, contact and communication in the virtual classroom. Journal of Educational Computing Research, 23(4), 359-383.
  • Swan, K. (2001). Virtual interaction: Design factors affecting student satisfaction and perceived learning in asynchronous online courses. Distance Education, 22 (2), 306-316.
  • Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics. Boston: Pearson.
  • Tallent-Runnels, M. K., Lan, W. Y., Fryer, W., Thomas, J. A., Cooper, S., & Wang, K. (2005). The relationship between problems with technology and graduate students' evaluations of online teaching. The Internet and higher education, 8(2), 167-174.
  • Tan, Ş. (2009). KR-20 ve Cronbach Alfa Katsayılarının Yanlış Kullanımları [Misuses of KR-20 and Cronbach’s Alpha Reliability Coefficients]. Eğitim ve Bilim, 34(152), 101-112.
  • Tehseen, S., Ramayah, T., & Sajilan, S. (2017). Testing and controlling for common method variance: A review of available methods. Journal of Management Sciences, 4(2), 142-168.
  • Ugaddan, R. G., & Park, S. M. (2017). Quality of leadership and public service motivation. International Journal of Public Sector Management, 30(3), 270-285.
  • West, J. (1994). Teaching public personnel management in three types of higher educational institutions. Review of Public Personnel Administration, 14(4), 22-38.
  • Yaşar, M. (2014). Bilimsel araştırma yöntemleri dersine yönelik tutum ölçeği geliştirme çalışması: Geçerlik ve güvenirlik [Developing an Attitude Scale Related to Scientific Research Methods Course: Validity and Reliability]. Eğitim Bilimleri Araştırma Dergisi, 4(2), 109-129.
  • Young, A. & Norgard, C. (2006). Assessing the quality of online courses from the students' perspective. Internet and Higher Education, 9, 107–115.

IDENTIFYING THE FACTORS AFFECTING PUBLIC ADMINISTRATION STUDENTS’ COURSE CONTENT EVALUATION

Year 2020, Volume: 12 Issue: 4, 332 - 344, 05.10.2020

Abstract

This study aims at exploring the elements affecting public administration students’ course content evaluation results. Empirical data were gathered from 171 public administration students from three different universities using the questionnaire method. Exploratory Factor Analysis, Cronbach's alpha test were used to analyze the data and the relationships were examined through Structural Equation Modeling. The current study describes a model in a reliable and valid way. In the study, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were both applied to identify clusters of variables to minimize the potential common method variance (CMV) effect. The study proved that there is a significant difference between public and foundation universities in terms of public administration students’ course content evaluation. Besides, student satisfaction and perceived significance both affect students’ evaluation of course content.

References

  • Alves, H., & Raposo, M. (2009). The measurement of the construct satisfaction in higher education. The Service Industries Journal, 29(2), 203-218.
  • Aoki, N. (2015). Institutionalization of New Public Management: The case of Singapore’s education system. Public Management Review, 17(2), 165-186.
  • Arbaugh, J. B. (2000). Virtual classroom characteristics and student satisfaction with internet-based MBA courses. Journal of Management Education, 24(1), 32-54.
  • Atchley, W., Wingenbach, G. & Akers, C. (2013). Comparison of course completion and student performance through online and traditional courses. The International Review of Research in Open and Distance Learning, 14(4), 104–116.
  • Banerjee, M. & Brinckerhoff, L. C. (2002). Assessing student performance in distance education courses: Implications for testing accommodations for students with learning disabilities. Assessment for Effective Intervention, 27(3), 25-35.
  • Bentler, P.M. (1990). Comparative fit indexes in structural equation models. Psychological Bulletin, 107, 238-246.
  • Betoret, F. D. (2007). The influence of students’ and teachers’ thinking styles on student course satisfaction and on their learning process. Educational Psychology, 27(2), 219-234.
  • Brans, M. & Coenen, L. (2016) The Europeanization of Public Administration teaching, Policy and Society, 35:4, 333-349.
  • Bright, L., & Graham Jr, C. B. (2016). Predictors of graduate student satisfaction in public administration programs. Journal of Public Affairs Education, 22(1), 17-34.
  • Byrne, B.M. (1998). Structural Equation Modeling with LISREL, PRELIS and SIMPLIS. NJ: Lawrence Erlbaum Associates.
  • Buckley, A. (2013). Engagement for enhancement: Report of a UK survey pilot. York: Higher Education Academy.
  • Chamillard, A. T., & Merkle, L. D. (2002). Evolution of an introductory computer science course: The long haul. Journal of Computing Sciences in Colleges, 18(1), 144-153.
  • Chan, A. P., Lam, P. T., Chan, D. W., Cheung, E., & Ke, Y. (2010). Critical success factors for PPPs in infrastructure developments: Chinese perspective. Journal of Construction Engineering and Management, 136(5), 484-494.
  • Colorado, J. T. & Eberle, J. (2010). Student demographics and success in online learning environments. Emporia State Research Studies, 46(1), 4–10.
  • De Carvalho, J., & Chima, F. O. (2014). Applications of structural equation modeling in social sciences research. American International Journal of Contemporary Research, 4(1), 6-11.
  • Durmuş, B., Yurtkoru, E. S., & Çinko, M. (2013). Sosyal Bilimlerde SPSS İle Veri Analizi. [Data Analysis in Social Sciences with SPSS]. (5th edition). İstanbul: Beta Publishing.
  • Ebdon, C. (1999). Teaching public finance administration online: A case study. Journal of Public Affairs Education, 5(3), 237-246.
  • Elliot, K. M., & Healy, M. A. (2001). Key factors influencing student satisfaction related to recruitment and retention. Journal of Marketing for Higher Education, 10, 1-11.
  • Finnie, R., Mueller, R. E., & Childs, S. (2010). Family Background, Cultural Capital and Access to Higher Education in Canada. Graduate School of Public and International Affairs. MESA Project (s. 9-12). Germany: XXIV Annual Conference of the European Society for Population Economics.
  • Fox, P., & Skitmore, M. (2007). Factors facilitating construction industry development. Building Research and Information, 35(2), 178-188.
  • Garg, M. (2018). Student satisfaction as determinant of academic success of distance learners: A study across distance learning courses. The Online Journal of Distance Education and e-Learning, 6(3), 30-43.
  • George, D., & Mallery, P. (2016). IBM SPSS Statistics 23 Step by Step: A Simple Guide and Reference. New York, NY: Routledge.
  • Gürbüz, S. (2019). Amos ile yapısal eşitlik modellemesi. Ankara: Seçkin.
  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). In J. F. Hair, Jr., W. C. Black, B. J. Babin, & R. E. Anderson (Eds.), Multivariate data analysis (Pearson new international seventh ed.). Harlow, Essex: Pearson.
  • Han, H., Kiatkawsin, K., Kim, W., & Hong, J. H. (2018). Physical classroom environment and student satisfaction with courses. Assessment & Evaluation in Higher Education, 43(1), 110-125.
  • Harris, G., Froman, J., & Surles, J. (2009). The professional development of graduate mathematics teaching assistants. International Journal of Mathematical Education in Science and Technology, 40(1), 157-172.
  • Hau-Siu Chow, I. (1995). Management education in Hong Kong: needs and challenges. International Journal of Educational Management, 9(5), 10-15.
  • Hearn, J. C. (1985). Determinants of college students' overall evaluations of their academic programs. Research in Higher Education, 23(4), 413-437.
  • Holtbrügge, D., & Engelhard, F. (2016). Study Abroad Programs: Individual Motivations, Cultural Intelligence, and the Mediating Role of Cultural Boundary Spanning. Academy of Management Learning & Education, 15(3), 435-455.
  • Horvat, A., Krsmanovic, M., & Djuric, M. (2012). Differences in students` satisfaction with distance learning studies. International Journal of Social, Education, Economics and Management Engineering, 6(6), 1412-1415.
  • Ijaz, A., Irfan, S. M., Shahbaz, S., Awan, M., & Sabir, M. (2011). An empirical model of student satisfaction: Case of Pakistani public sector business schools. Journal of quality and Technology Management, 7(2), 91-114.
  • Irani, T. (1998). Communication potential, information richness and attitude: A study of computer mediated communication in the ALN classroom. ALN magazine, 2(1), 1-12.
  • Jakobsen, M., & Jensen, R. (2015). Common method bias in public management studies. International Public Management Journal, 18(1), 3–30.
  • Keller, J. (1983). Motivational design of instruction. In Reigeluth, C. M. (Ed.), Instructional design theories and models: An overview of their current status (1st ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.
  • Kline, P. (1994). An Easy Guide to Factor Analysis. London: Routledge.
  • Kline, R. B. (1998). Principles and Practice of Structural Equation Modeling. New York: The Guilford Press.
  • Liem, G. A. D. (2019). Academic performance and assessment. Educational Psychology, 39(6), 705-708. DOI: 10.1080/01443410.2019.1625522
  • Lopez-Littleton, V. & Blessett, B. (2015). A Framework for Integrating Cultural Competency into the Curriculum of Public Administration Programs, Journal of Public Affairs Education, 21:4, 557-574, DOI: 10.1080/15236803.2015.12002220
  • Medsker, L., & Turban, E. (1994). Integrating expert systems and neural computing for decision support. Expert Systems with Applications, 7(4), 553-562.
  • Min, S., & Mentzer, J. T. (2004). Developing and measuring supply chain management concepts. Journal of business logistics, 25(1), 63-99.
  • Moore, M. G. (1989). Editorial: Three types of interaction, The American Journal of Distance Education. 3(2), 1-6.
  • Moore, M. G., & Kearsley, G. (1996). Distance education: A systems view. New York, NY: Wadsworth.
  • Nu’man, A. (2012). A Framework for Adopting E-Voting in Jordan. Electronic Journal of e-Government, 10(2), 133-146.
  • Özdamar, K. (1999). Paket Programlar İle İstatistiksel Veri Analizi [Statistical data analysis by custom softwares]. Eskişehir: Kaan Publishing.
  • Özdemirci, A., Özcan, E. D., & İldaş, G. (2014). The Relationship between World Views of Rectors with Corporate. International Journal of Business and Management, 9(1), 149-167.
  • Pallant, J. (2001). SPSS survival manual: A step-by-step guide to data analysis using SPSS for windows. Philadelphia, PA: Open University Press.
  • Pike, G. R. (1993). The relationship between perceived learning and satisfaction with college: An alternative view. Research in Higher Education, 34(1), 23-40.
  • Podsakoff, P.M., MacKenzie, S.B., Lee, J.Y. and Podsakoff, N.P. (2003), “Common method biases in behavioral research: a critical review of the literature and recommended remedies”, Journal of Applied Psychology, 88(5), 879-903.
  • Powell, D. C. (2007). Student satisfaction with a distance learning MPA program: A preliminary comparison of on-campus and distance learning students’ satisfaction with MPA courses. MERLOT Journal of Online Learning and Teaching, 3 (1), 1-18.
  • Rissi, J.J. & Gelmon, S.B. (2014). Development, Implementation, and Assessment of a Competency Model for a Graduate Public Affairs Program in Health Administration, Journal of Public Affairs Education, 20:3, 335-352.
  • Romiszowski, A. (2004). How’s the e-learning baby? Factors leading to success or failure of an educational technology innovation. Educational Technology, 44(1), 5–27.
  • Rubaii, N. (2019). Why research methods matter: Essential skills for decision-making. Journal of Public Affairs Education, 25:2, 277-279, DOI: 10.1080/15236803.2019.1565253
  • Sakri, S., Salim, J. & Sembok, T.M.T. (2012). Information Communications and Technology (ICT) Abuse in the Malaysian Public Sector: The Influence of Ethical, Organisational Bond and General Deterrence Factors. Akademika 82(1), 125-137.
  • Santor, D. A., Haggerty, J. L., Lévesque, J. F., Burge, F., Beaulieu, M. D., Gass, D., & Pineault, R. (2011). An overview of confirmatory factor analysis and item response analysis applied to instruments to evaluate primary healthcare. Healthcare Policy, 7(Special Issue), 79 - 92.
  • Schram, C. M. (1996). A meta-analysis of gender differences in applied statistics achievement. Journal of Educational and Behavioral Statistics, 21(1), 55-70.
  • Shea, P. J., Pickett, A. M., & Pelz, W. E. (2003). A follow-up investigation of teaching presence in the SUNY learning network. Journal of Asynchronous Learning Networks, 7(2), 61–80.
  • Shur, D.D. (2006). Exploratory or Confirmatory Factor Analysis? Statistics and Data Analysis, SUGI 31. Retrieved January 11, 2018, from http://tx.liberal.ntu.edu.tw/~PurpleWoo/Literature/!DataAnalysis/FactorAnalysis_SAS.com_200-31.pdf.
  • Simpson, C., & Du, Y. (2004). Effects of learning styles and class participation on students' enjoyment level in distributed learning environments. Journal of Education for Library and Information Science, 123-136.
  • Swan, K., Shea, P., Fredericksen, E., Pickett, A., Pelz, W., & Maher, G. (2000). Building knowledge building communities: Consistencies, contact and communication in the virtual classroom. Journal of Educational Computing Research, 23(4), 359-383.
  • Swan, K. (2001). Virtual interaction: Design factors affecting student satisfaction and perceived learning in asynchronous online courses. Distance Education, 22 (2), 306-316.
  • Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics. Boston: Pearson.
  • Tallent-Runnels, M. K., Lan, W. Y., Fryer, W., Thomas, J. A., Cooper, S., & Wang, K. (2005). The relationship between problems with technology and graduate students' evaluations of online teaching. The Internet and higher education, 8(2), 167-174.
  • Tan, Ş. (2009). KR-20 ve Cronbach Alfa Katsayılarının Yanlış Kullanımları [Misuses of KR-20 and Cronbach’s Alpha Reliability Coefficients]. Eğitim ve Bilim, 34(152), 101-112.
  • Tehseen, S., Ramayah, T., & Sajilan, S. (2017). Testing and controlling for common method variance: A review of available methods. Journal of Management Sciences, 4(2), 142-168.
  • Ugaddan, R. G., & Park, S. M. (2017). Quality of leadership and public service motivation. International Journal of Public Sector Management, 30(3), 270-285.
  • West, J. (1994). Teaching public personnel management in three types of higher educational institutions. Review of Public Personnel Administration, 14(4), 22-38.
  • Yaşar, M. (2014). Bilimsel araştırma yöntemleri dersine yönelik tutum ölçeği geliştirme çalışması: Geçerlik ve güvenirlik [Developing an Attitude Scale Related to Scientific Research Methods Course: Validity and Reliability]. Eğitim Bilimleri Araştırma Dergisi, 4(2), 109-129.
  • Young, A. & Norgard, C. (2006). Assessing the quality of online courses from the students' perspective. Internet and Higher Education, 9, 107–115.
There are 68 citations in total.

Details

Primary Language English
Journal Section Research Article
Authors

Burak Hergüner 0000-0002-6980-5339

Publication Date October 5, 2020
Submission Date June 22, 2020
Acceptance Date July 13, 2020
Published in Issue Year 2020 Volume: 12 Issue: 4

Cite

APA Hergüner, B. (2020). IDENTIFYING THE FACTORS AFFECTING PUBLIC ADMINISTRATION STUDENTS’ COURSE CONTENT EVALUATION. İstanbul Aydın Üniversitesi Dergisi, 12(4), 332-344.

All site content, except where otherwise noted, is licensed under a Creative Common Attribution Licence. (CC-BY-NC 4.0)

by-nc-300x105-1.png