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Academic Retention in the Italian Context

  • Maria Lidia MasciaEmail author
  • Mirian Agus
  • Gianrico Dettori
  • Maria Assunta Zanetti
  • Eliano Pessa
  • Maria Pietronilla Penna
Chapter

Abstract

This study analyzes if motivation, academic self-concept, perception of the time perspective, self-regulation, and the attendance of specific online laboratory activities influence academic retention and achievement of two group of freshmen attending the first year of their Bachelor’s Degree. The freshmen were monitored along their first academic year. In particular, we try to understand which factors can help student to overcome the transition gap created by the passage from high school to university. The choice of the implementation of an online lab is due to evidence that online platforms are tools that can help to reduce the academic dropout. These platforms allowed students to use a supporting network, but, at the same time, students can autonomously take advantage of suitable materials to achieve their learning goals and to bridge an orientation gap. In Italy, this gap is often present in the transition between high school and university. In general, we can say that the experience of the online laboratory was positive and combined with the enhancement of motivation, academic self-concept, vision of the time perspective, and self-regulation can represent an important support above all for the Italian freshmen.

Keywords

Dropout Online laboratory Freshmen Academic retention Italian context 

References

  1. 1.
    Aina, C. (2013). Parental background and university dropout in Italy. Higher Education, 65(4), 437–456.CrossRefGoogle Scholar
  2. 2.
    Allen, D. (1999). Desire to finish college: An empirical link between motivation and persistence. Research in Higher Education, 40, 461–485.CrossRefGoogle Scholar
  3. 3.
    Byrne, M., Flood, B., & Griffin, J. (2014). Measuring the academic self-efficacy of first-year accounting students. Accounting Education, 23(5), 407–423.CrossRefGoogle Scholar
  4. 4.
    Alivernini, F., & Lucidi, F. (2011). Relationship between social context, self-efficacy, motivation, academic achievement, and intention to drop out of high school: A longitudinal study. The Journal of Educational Research, 104(4), 241–252.CrossRefGoogle Scholar
  5. 5.
    Venuleo, C., Mossi, P., & Salvatore, S. (2016). Educational subculture and dropping out in higher education: A longitudinal case study. Studies in Higher Education, 41(2), 321–342.CrossRefGoogle Scholar
  6. 6.
    Turri, M. (2016). The difficult transition of the Italian university system: Growth, underfunding and reforms. Journal of Further and Higher Education, 40(1), 83–106.CrossRefGoogle Scholar
  7. 7.
    Yorke, M., & Longden, B. (2008). The first-year experience of higher education in the UK. York: Higher Education Academy.Google Scholar
  8. 8.
    Clerici, R., Giraldo, A., & Meggiolaro, S. (2015). The determinants of academic outcomes in a competing risks approach: Evidence from Italy. Studies in Higher Education, 40(9), 1535–1549.CrossRefGoogle Scholar
  9. 9.
    Brunner, M., Keller, U., Dierendonck, C., Reichert, M., Ugen, S., Fischbach, A., & Martin, R. (2010). The structure of academic self-concepts revisited: The nested Marsh/Shavelson model. Journal of Educational Psychology, 102(4), 964–981.  https://doi.org/10.1037/a0019644 CrossRefGoogle Scholar
  10. 10.
    de Bilde, J., Vansteenkiste, M., & Lens, W. (2011). Understanding the association between future time perspective and self-regulated learning through the lens of self-determination theory. Learning and Instruction, 21(3), 332–344.CrossRefGoogle Scholar
  11. 11.
    Lehmann, T., Hähnlein, I., & Ifenthaler, D. (2014). Cognitive, metacognitive and motivational perspectives on preflection in self-regulated online learning. Computers in Human Behavior, 32, 313–323.CrossRefGoogle Scholar
  12. 12.
    Deci, E. L., & Ryan, R. M. (2002). Handbook of self-determination research. New York: University of Rochester Press.Google Scholar
  13. 13.
    Ormrod, J. (2008). Human learning. New Jersey: Pearson Education.Google Scholar
  14. 14.
    Deci, E. L., & Ryan, R. M. (2008). Self-determination theory: A macrotheory of human motivation, development, and health. Canadian Psychology, 49(3), 182–185.  https://doi.org/10.1037/a0012801 CrossRefGoogle Scholar
  15. 15.
    Deci, E. L., & Ryan, R. M. (2012). Motivation, personality, and development within embedded social contexts: An overview of self-determination theory. In R. M. Ryan (Ed.), The Oxford handbook of human motivation (pp. 85–107). Oxford, England: Oxford University.Google Scholar
  16. 16.
    Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. New York: Plenum.CrossRefGoogle Scholar
  17. 17.
    Vallerand, R. J., Pelletier, L. G., Blais, M. R., Briere, N. M., Senecal, C., & Vallieres, E. F. (1992). The academic motivation scale: A measure of intrinsic, extrinsic, and amotivation in education. Educational and Psychological Measurement, 52(4), 1003–1017.CrossRefGoogle Scholar
  18. 18.
    Vallerand, R. J. (1997). Toward a hierarchical model of intrinsic and extrinsic motivation. Advances in Experimental Social Psychology, 29, 271–360.CrossRefGoogle Scholar
  19. 19.
    Grouzet, F. M., Otis, N., & Pelletier, L. (2006). Longitudinal cross-gender factorial invariance of the academic motivation scale. Structural Equation Modeling, 13(1), 73–98.CrossRefGoogle Scholar
  20. 20.
    Walls, T. A., & Little, T. D. (2005). Relations among personal agency, motivation, and school adjustment in early adolescence. Journal of Educational Psychology, 97(1), 23–31.CrossRefGoogle Scholar
  21. 21.
    Calsyn, R. J., & Kenny, D. A. (1977). Self-concept of ability and perceived evaluation of others: Cause or effect of academic achievement? Journal of Educational Psychology, 69(2), 136–143.CrossRefGoogle Scholar
  22. 22.
    Marsh, H. W. (1990). A multidimensional, hierarchical self-concept: Theoretical and empirical justification. Educational Psychology Review, 2, 77–172.CrossRefGoogle Scholar
  23. 23.
    Marsh, H. W., & Scalas, L. F. (2011). Self-concept in learning: Reciprocal effects model between academic self-concept and academic achievement. In S. Järvelä (Ed.), Social and emotional aspects of learning (pp. 191–198). Oxford, England: Academic Press.Google Scholar
  24. 24.
    Marsh, H. W., & Craven, R. G. (2006). Reciprocal effects of self-concept and performance from a multidimensional perspective: Beyond seductive pleasure and unidimensional perspectives. Perspectives on Psychological Science, 1, 133–163.CrossRefGoogle Scholar
  25. 25.
    Marsh, H. W. (2007). Self-concept theory, measurement and research into practice: The role of self-concept in educational psychology. Leicester, UK: British Psychological Society.Google Scholar
  26. 26.
    Marsh, H. W., & O’Mara, A. J. (2008). Reciprocal effects between academic self-concept, self-esteem, achievement, and attainment over seven adolescent years: Unidimensional and multidimensional perspectives of self-concept. Personality and Social Psychology Bulletin, 34, 542–552.CrossRefGoogle Scholar
  27. 27.
    Zimbardo, P. G., & Boyd, J. N. (1999). Putting time in perspective: A valid, reliable individual-differences metric. Journal of Personality and Social Psychology, 77(6), 1271–1288.CrossRefGoogle Scholar
  28. 28.
    Leondari, A. (2007). Future time perspective, possible selves, and academic achievement. New Directions for Adult and Continuing Education, 114, 17–26.CrossRefGoogle Scholar
  29. 29.
    Phan, H. P. (2009). Amalgamation of future time orientation, epistemological beliefs, achievement goals and study strategies: Empirical evidence established. British Journal of Educational Psychology, 79(1), 155–173.CrossRefGoogle Scholar
  30. 30.
    Eilam, B., & Aharon, I. (2003). Students’ planning in the process of self-regulated learning. Contemporary Educational Psychology, 28(3), 304–334.CrossRefGoogle Scholar
  31. 31.
    Zimmerman, B. J., & Martinez-Pons, M. (1988). Construct validation of a strategy model of student self-regulated learning. Journal of Educational Psychology, 80(3), 284–290.CrossRefGoogle Scholar
  32. 32.
    Zimmerman, B. J., & Schunk, D. H. (Eds.). (2011). Handbook of self-regulation of learning and performance. New York: Taylor & Francis.Google Scholar
  33. 33.
    Ifenthaler, D. (2013). Cognitive, metacognitive and motivational perspectives on preflection in self-regulated online learning. Computers in Human Behavior, 72(2), 231–245.Google Scholar
  34. 34.
    Ifenthaler, D., & Lehmann, T. (2012). Preactional self-regulation as a tool for successful problem solving and learning. Technology, Instruction, Cognition and Learning, 9(1/2), 97–110.Google Scholar
  35. 35.
    Thillmann, H., Künsting, J., Wirth, J., & Leutner, D. (2009). Is it merely a question of “what” to prompt or also “when” to prompt? Zeitschrift für Pädagogische Psychologie, 23(2), 105–115.CrossRefGoogle Scholar
  36. 36.
    Boekaerts, M., & Corno, L. (2005). Self-regulation in the classroom: A perspective on assessment and intervention. Applied Psychology, 54(2), 199–231.CrossRefGoogle Scholar
  37. 37.
    Zimmerman, B. J. (2008). Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American Educational Research Journal, 45(1), 166–183.CrossRefGoogle Scholar
  38. 38.
    Zimmerman, B. J., & Schunk, D. H. (Eds.). (2013). Self-regulated learning and academic achievement: Theoretical perspectives. London: Routledge.Google Scholar
  39. 39.
    Kosnin, A. M. (2007). Self-regulated learning and academic achievement in Malaysian undergraduates. International Education Journal, 8(1), 221–228.Google Scholar
  40. 40.
    Albanese, O., Businaro, N., Cacciamani, S., De Marco, B., Farina, E., Ferrini, T., & Vanin, L. (2010). Riflessione metacognitiva in ambienti online e autoregolazione nell’attività di studio nei corsi universitari. TD - Tecnologie Didattiche, 49(1), 50–61.Google Scholar
  41. 41.
    Flavell, J. (1979). Metacognition and cognitive monitoring: A new area of cognitive developmental inquiry. American Psychologist, 34, 906–911.CrossRefGoogle Scholar
  42. 42.
    Ridley, D. S., Schutz, P. A., Glanz, R. S., & Weinstein, C. E. (1992). Self-regulated learning: The interactive influence of metacognitive awareness and goal-setting. The Journal of Experimental Education, 60(4), 293–306.CrossRefGoogle Scholar
  43. 43.
    Ross, M. E., Green, S. B., Salisbury-Glennon, J. D., & Tollefson, N. (2006). College students’ study strategies as a function of testing: An investigation into metacognitive self-regulation. Innovative Higher Education, 30(5), 361–375.CrossRefGoogle Scholar
  44. 44.
    Borkowski, J. G., Johnston, M. B., & Reid, M. K. (1986). Metacognition, motivation, and controlled performance. Handbook of Cognitive, Social, and Neuropsychological Aspects of Learning Disabilities, 2, 147–174.Google Scholar
  45. 45.
    Boekaerts, M. (1997). Self-regulated learning: A new concept embraced by researchers, policy makers, educators, teachers, and students. Learning and Instruction, 7(2), 161–186.CrossRefGoogle Scholar
  46. 46.
    Cobb, P., Confrey, J., Lehrer, R., & Schauble, L. (2003). Design experiments in educational research. Educational Researcher, 32(1), 9–13.CrossRefGoogle Scholar
  47. 47.
    Mohamad, S. K., Tasir, Z., Harun, J., & Shukor, N. A. (2013). Pattern of reflection in learning authoring system through blogging. Computers e Education, 69, 356–368.CrossRefGoogle Scholar
  48. 48.
    Antonietti, A., & Cantoia, M. (2001). Imparare con il computer. Trento: Erickson.Google Scholar
  49. 49.
    Dembo, M. H., & Seli, H. (2008). Motivation and learning strategies for college success. London: Routledge.Google Scholar
  50. 50.
    Boekaerts, M. (1999). Self-regulated learning: Where we are today. International Journal of Educational Research, 31(6), 445–457.CrossRefGoogle Scholar
  51. 51.
    Rosario, P., Nuñez Perez, J. C., & González-Pienda, J. A. (2004). Stories that show how to study and how to learn: An experience in the Portuguese school system. Electronic Journal of Research in Educational Psychology, 2(1), 131–144.Google Scholar
  52. 52.
    Azevedo, R., & Hadwin, A. F. (2005). Introduction to special issue: Scaffolding self-regulated learning and metacognition: Implications for the design of computer-based scaffolds. Instructional Science, 33, 367–379.CrossRefGoogle Scholar
  53. 53.
    Giannetti, T. (2006). Autoregolazione dell’apprendimento e tecnologie didattiche. Tecnologie Didattiche, 37, 51–56.Google Scholar
  54. 54.
    Conati, C., & Vanlehn, K. (2000). Toward computer-based support of meta-cognitive skills: A computational framework to coach self-explanation. International Journal of Artificial Intelligence in Education (IJAIED), 11, 389–415.Google Scholar
  55. 55.
    Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into Practice, 41(2), 64–70.CrossRefGoogle Scholar
  56. 56.
    Brummernhenrich, B., & Jucks, R. (2013). Managing face threats and instructions in online tutoring. Journal of Educational Psychology, 105(2), 341–350.CrossRefGoogle Scholar
  57. 57.
    Greene, J. A., & Azevedo, R. (2009). A macro-level analysis of SRL processes and their relations to the acquisition of a sophisticated mental model of a complex system. Contemporary Educational Psychology, 34(1), 18–29.CrossRefGoogle Scholar
  58. 58.
    De Marco, B., & Albanese, O. (2010). 11. Le competenze autoregolative dell’attività di studio in comunità virtuali. QWERTY-Interdisciplinary Journal of Technology, Culture and Education, 4(2), 123–139.Google Scholar
  59. 59.
    D’Alessio, M., Guarino, A., De Pascalis, V., & Zimbardo, P. G. (2003). Testing Zimbardo’s Stanford time perspective inventory (STPI)-short form an Italian study. Time e Society, 12(2-3), 333–347.CrossRefGoogle Scholar
  60. 60.
    Alivernini, F., & Lucidi, F. (2008). The academic motivation scale (AMS): Factorial structure, invariance and validity in the Italian context. Testing, Psychometrics, Methodology in Applied Psychology, 15(4), 211–220.Google Scholar
  61. 61.
    Marsh, H. W., & O’Neill, R. (1984). Self description questionnaire III: The construct validity of multidimensional self-concept ratings by late adolescents. Journal of Educational Measurement, 21(2), 153–174.CrossRefGoogle Scholar
  62. 62.
    Shavelson, R. J., Hubner, J. J., & Stanton, G. C. (1976). Self-concept: Validation of construct interpretations. Review of Educational Research, 46(3), 407–441.CrossRefGoogle Scholar
  63. 63.
    Moè, A., & De Beni, R. (2000). Strategie di autoregolazione e successo scolastico: Uno studio con ragazzi di scuola superiore e universitari (self-regulation strategies and academic achievement: A research with high school and college students). Psicologia dell’Educazione e della Formazione, 2, 31–44.Google Scholar
  64. 64.
    Ley, K., & Young, D. B. (1998). Self-regulation behaviors in underprepared (developmental) and regular admission college students. Contemporary Educational Psychology, 23(1), 42–64.CrossRefGoogle Scholar
  65. 65.
    Mattana, V. (2014). L’e-tutor in Italia: Una rassegna della letteratura scientifica. Form@re, 14(1), 38–48.Google Scholar
  66. 66.
    Michinov, N., Brunot, S., Le Bohec, O., Juhel, J., & Delaval, M. (2011). Procrastination, participation, and performance in online learning environments. Computers & Education, 56(1), 243–252.CrossRefGoogle Scholar
  67. 67.
    Rotta, M., & Ranieri, M. (2005). E-tutor: Identità e competenze. Un profilo professionale per l’e-learning. Trento: Erickson.Google Scholar
  68. 68.
    Dougiamas, M. (2004). Moodle: A virtual learning environment for the rest of us. TESL-EJ, 8(2), 1–8.Google Scholar
  69. 69.
    de Palo, V., Sinatra, M., Tanucci, G., & Monacis, L. (2012). Self-regulated strategies in an e-learning environment. Procedia - Social and Behavioral Sciences, 69, 492–501.CrossRefGoogle Scholar
  70. 70.
    Grau-Valldosera, J., & Minguillón, J. (2014). Rethinking dropout in online higher education: The case of the Universitat Oberta de Catalunya. The International Review of Research in Open and Distributed Learning, 15(1).Google Scholar
  71. 71.
    Yukselturk, E., Ozekes, S., & Türel, Y. K. (2014). Predicting dropout student: An application of data mining methods in an online education program. European Journal of Open, Distance and E-learning, 17(1), 118–133.Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Maria Lidia Mascia
    • 1
    Email author
  • Mirian Agus
    • 1
  • Gianrico Dettori
    • 1
  • Maria Assunta Zanetti
    • 2
  • Eliano Pessa
    • 2
  • Maria Pietronilla Penna
    • 1
  1. 1.Department of Pedagogy, Psychology, PhilosophyUniversity of CagliariCagliariItaly
  2. 2.Department of Brain and Behavioural SciencesUniversity of PaviaPaviaItaly

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