22 SES 07 B, Students at Risk: How to avoid drop-outs
Student engagement is considered word-wild as a key factor in student retention in higher education. Student engagement is defined as a student’s academic commitment and shown in time and energy devoted to activities that lead success in completing studies (Crosling et al., 2009). Student engagement can be influenced by psycho-social, institutional and socio-cultural variables and different models are available to interpret the academic dropout in higher education.
Within the framework of the adaptation model of student retention, Holland’s (1985) model indicates that environmental adaptation and personal success depend on the characteristics of the student's personality type (RIASEC model, meaning Realistic, Investigative, Artistic, Social, Enterprising, and Conventional). The personality types are based on the assumption that correspondence between key personality characteristics and work environments leads to important vocational outcomes (e.g. satisfaction, performance), meaning that certain characteristics favour adaptation while others do not. Consequently, inadequate vocational choices can lead to low engagement and demotivation that finally cause the dropout of studies (Cabrera, et al., 2006).
The institution’s responsibility is crucial in the student’s decision to drop out (Thomas, 2002), as faculty’s practices, teaching methods that promote active learning are critical aspects contributing to the students’ persistence (Cabrera, et al., 2005). In his influential theory of persistence, Tinto (1975, 2005) suggests that student integration is a very important factor for retention. Social integration can be evaluated by the level of positive interactions between peers and faculty members and without sufficient integration students can be isolated themselves and be at risk of drop out. Tinto (1998) also emphasizes the role of learning communities that facilitates cooperative work.
There is an interrelationship between student engagement, the quality of student learning and the teaching and learning context (Brysen and Hand, 2007), and student engagement can be improved by including active and interactive orientation in teaching. Active learning is associated with experiential, problem-based and project-based learning, and other forms of collaborative learning and group activities (Warren, 2003). Implementation of cooperative learning method in higher education (Millis and Cottell, 1998) is beneficial for individual students from both social-behavioural and cognitive-developmental perspective as it increases motivation to achieve goals, promotes interaction between members, and consequently enhances learning outcome. Cooperation is a key element of inclusion in educational context (Putnam, 1998).
This paper reflects the first phase of a longitudinal study and its purpose is to identify the competencies and needs for development of first-year Engineering Informatics students in order to find the possible ways to adapt teaching and learning techniques to their characteristics and to increase retention rates of newly enrolled students of Óbuda University in Hungary. This paper addresses the following research questions: How do Engineering Informatics undergraduates perceive their competencies at the beginning of their academic career and how do they rate the importance of these competencies for their future employment? Which personal and social competencies need to be developed during their studies? How do their personality characteristics match their choice of academic major?
The research was designed to measure a wide range of attributes and competencies (e.g. learning style, logical thinking, emotional intelligence, personality profile, achievement motivation, self-efficacy) in three time points among Engineering Informatics students during their academic career. This paper focuses on the newly enrolled students’ competencies and personality traits, and their needs of competency development. A sample of 475 first-year Engineering Informatics students attending a Hungarian university participated in the study, of whom 432 were males (90.9%) and 43 were females (9.1%). The ages of participants ranged from 18 to 35 years (M=20.15). The data was collected via a set of questionnaires in two time points, since 188 students were participated in September 2016 and 287 students were involved in September 2017 before starting the first semester. The competencies of Engineering Informatics students were measured with a self-rating list of competencies consisting of 24 items which was constructed by using job vacancy advertisements and the results of previous competency assessments in higher education in Hungary. Using a 5-point Likert scale we examined, on the one hand, the extent to which these competencies are necessary for the future job according to the opinion of Engineering Informatics students, and, on the other hand, the extent to which they possess these competencies at the beginning of their studies. The two sets of competencies showed acceptable internal consistency (Cronbach’s alpha: 0.860 és 0.872). Personality traits of Engineering Informatics students, based on the Big Five model, were assessed with the Hungarian version of Big Five Questionnaire (BFQ, Caprara et al., 1999). The BFQ measures five personality factors, namely energy, friendliness, conscientiousness, emotional stability and openness. The questionnaire consists of 132 items, with five dimensions and ten sub-scales, and a social desirability scale. The internal consistency coefficient of the questionnaire was feasible (Cronbach alpha: 0.86). For data analysis statistical methods as Chi-square and Wilcoxon tests, Crosstabs statistics, Pearson correlation and ANOVA were performed using SPSS program.
The findings related to competencies indicate that Engineering Informatics students estimate their skills as very good at applying knowledge, understanding causal relationships, adaptation to change, but obtain lower ratings are found for organization, oral communication and written communication. They attribute less importance to writing ability, self-knowledge and conflict management in their future work. Extreme differences are detected between the level of actual skills and the level expected by the workplace in concentration, learning ability and problem-solving. Regarding personality traits, the Engineering Informatics students display a low level of social skills, as they are introvert, prefer working independently and tend to avoid interactions with others. They are characterized by moderate friendliness and cooperation, but higher level of conscientiousness and emotional stability. Surprisingly, 54.3% of the students obtained very low scores on openness scale, meaning that they are not really open to changes and innovations, rather they have a traditional and conventional way of thinking. Based on BFQ profiles, the Holland’s personality types can be derived: 32% of the sample can be labeled as Realistic or Conventional type, meaning that only a third of students have chosen the academic major in accordance with their personality traits. Practical implication of our results indicate that the competency development of Engineering Informatics students should include the development of self-knowledge and several personal and interpersonal skills which mainly contribute to the students’ academic career and future success in the workplace. The improvement of learning abilities, concentration and problem-solving skills are also crucial for preventing dropout among these students. Our findings suggest that identifying the competencies and personality traits of the first-year Engineering Informatics students and adapting student-centered learning approach based on their needs can be the first step to realize the idea of inclusion and to improve student retention in engineering higher education.
1.Bryson, C., Hand, L. (2007): The Role of engagement in inspiring teaching and learning. Innovations in Education and Teaching International. 44 (4) pp. 349-363 2.Cabrera, L., Tomás, J., Álvarez, P., Gonzalez, M. (2006): El problema del abandono de los estudios universitarios. RELIEVE, 12(2), 171-203. 3.Caprara, G. V., Barbaranelli, C., Borgogni, L., Perugini, M. (1993): The "big five questionnaire:" A new questionnaire to assess the five factor model. Personality and Individual Differences, 15(3), 281-288. 4.Crosling, G, Heagney, M, Thomas, L (2009): Improving student retention in higher education, Australian Universities’ Review, vol. 51, no. 2 5.Holland, J. L. (1985): Making vocational choices: A theory of vocational personalities and work environments. (2nd ed.). Englewood Cliffs, NJ: Prentice-Hall 6.Millis, B. J., Cottell, P. G. (1998): Cooperative Learning for higher education faculty. Oryx Press. 7.Putnam, J. W. (1998): Cooperative Learning and Strategies for Inclusion: Celebrating Diversity in the Classroom. Paul H. Brookes Publishing 8.Tinto, V. (1998): Learning Communities and the Reconstruction of Remedial Education in Higher Education, Replacing Remediation in Higher Education Conference, Stamford University, Jan 26-27 9.Tinto, V. (2005): Reflections on retention and persistence: Institutional actions on behalf of student persistence. Studies in Learning, Evaluation, Innovation and Development. 2(3), pp. 89-97 10.Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent re-search. Review of Educational Research, 45, 89-125. 11.Warren, D. (2003): Improving student retention: A team approach. Annual Conference of the Institute for Learning and Teaching in HE, University of Warwick, Coventry, 2-4 July, 2003
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