Students’ educational performance and drop out factors in the Degree Programme in Business Information Technology (BITE)
Haaga-Helia Julkaisutoiminta 15.5.2018

Students’ performance in educational institutes depends on many factors. These factors are classified as internal and external factors. The internal factors are based on individual psychology and external factors depend on the external factors such as educational environment, family setups, economy, and other contextual factors. In addition to all these factors, economic recession, educational financial challenges and national policy also affect students’ educational performance. These factors are often the core reason for students’ dropout from universities. The awareness of these factors by degree programmes and educational institutes help to anticipate proper measures and strategies. In other words, to predict and prevent discontinuation, behavioral engagement and academic performance helps to reduce academic withdrawal rates.

This short article only focuses on students’ academic performance in the Haaga-Helia University of Applied Sciences’ Degree Programme in Business Information Technology (BITE). This report is based on the information gathered from BITE degree programme students during Autumn 2017 by using a questionnaire. This study and the questionnaire are based on Niemivirta’s (2002) study. For analyzing the data, we applied various statistical analyses, pattern recognition, and a machine-learning algorithm to be able to predict the future trend. In this paper, however, we only present the statistical analysis. This information helps the decision makers to anticipate the dropouts and tackle factors such as fear of failure and academic withdrawal.

 

1 Students’ goal-orientation and educational performance

The conducted study is based (Niemivirta 2002) on eight scales on of motivational factors.

  1. Mastery-intrinsic orientation, which measures students’ inner motivation and interest in studying.
  2. Mastery-extrinsic orientation, which measures students’ motivation to perform well in their studies.
  3. Performance-intrinsic orientation, which measures students’ tendency to outperform their peers.
  4. Performance-extrinsic orientation, which measures students’ tendency to avoid making mistakes or fail in the course.
  5. Avoidance orientation, which measures students’ efforts regarding school work (make as little effort as possible as for school work)
  6. Academic withdrawal, which measures students’ tendency to give up studies or to dropout from school.
  7. Fear of failure, which measures students’ fear towards academic performance.
  8. School value, which measures how students appreciate and value academic institutes.

 

2 Research questions and research method

To assess BITE students’ motivational factors, we prepared a questionnaire based on Niemivirta’s study. The questionnaire was then delivered as a hard copy to students in different semesters. We defined the following three main research questions as:

  1. Why a student with a strong goal orientation is less likely to drop out from school?
  2. Do students with different socio-geographical characteristics have different patterns of motivational beliefs?
  3. What approach Haaga-Helia UAS needs to take to anticipate dropouts?

We analyzed the gathered data by applying various quantitative data analyses approaches. However, presenting all the data and details about our findings is excluded from this short report as they are considered out of the scope of this report. We are, however, in the process of publishing the details in an educational psychological journal.

 

3 Result

The data was collected by handing over the questionnaires to 98 BITE degree students in various semesters. The questionnaire contained thirty different statements and each question had a 1–7-point scale where students were asked to choose how much they agreed or disagreed with the a statement. In order to measure the relations between different factors, we defined eight clusters (please see section 1) which each cluster consisted of three related statements.

In accordance with the data, most of the students have high mastery orientation and high appreciation of the school (which means the school is valued highly). Half of the students choose an average (3–5 points) among statements related to performance orientation, which is a similar result as regarding the fear of failure and avoidance orientation scales.

Table 1 presents the means, standard deviations and correlation for motivational variables. The mean value of mastery-intrinsic orientation (m=6.42) is the highest, following is school value (m=6.02). Academic withdrawal (m=3.05) has the lowest mean value, following avoidance orientation (m=3.16). Mastery-intrinsic orientation and mastery-extrinsic orientation correlated negatively with academic withdrawal and avoidance orientation, which means that students with strong goal orientation are less likely to drop out. What’s more, students with different socio-geographical characteristics have different patterns of motivational beliefs.

Motivational variables
Table 1. Means, standard deviations and correlations for motivational variables.

4 Answers to research questions

  1. Why a student with a strong goal orientation is less likely to drop out from the school?

In the BITE degree programme, students in general feel more mastery-intrinsic orientation and mastery-extrinsic orientations. This correlates negatively with academic withdrawal and avoidance orientation. In other words, students who have a strong goal orientation (mastery-intrinsic and mastery extrinsic orientation) are less likely to withdraw from school and put more effort on school tasks and assignments. This is obvious as for the first and second semester students, but from third semester on, this correlation changes. The reason will be explained later in this paper.

  1. Do students with different socio-geographical characteristics have different patterns of motivational beliefs?

The results indicate that BITE students with various socio-geographical characteristics have different patterns of motivational beliefs. For example, female students have a stronger mastery-extrinsic orientation and performance-approach orientation than male students. This means that the female students in BITE are more outperformers than the male student in BITE. In addition, BITE female students have a higher fear-of-failure than the male BITE students. Since the fear-of-failure is associated with academic withdrawal, female students have a higher tendency to withdraw from studies.

Furthermore, the study indicates that male BITE students have higher avoidance orientation than female students. Female students are more comfortable with achieving academic excellence and male students are goal-oriented but they tend to make less effort. This also becomes evident by  the fact that the majority of BITE students who continue their education at Aalto University are female students. Students from different semesters had their unique pattern of motivational beliefs. Students in seventh semester had the highest fear-of-failure, but the sample was too small to draw a general conclusion.

An interesting point, which is highlighted in the data, is that the second semester BITE students are characterized with high levels of mastery-intrinsic orientation. However, in the third semester the mastery – intrinsic drop to lowest and the score of academic withdrawal and performance-avoidance orientation reached the maximum. This maybe results from the fact that students have to find work placement. Finding work placement is associated with challenges such as students’ motivation level, confusion, self-confidence, and competences. Therefore, academic advisor and BITE degree programme director would be wise to pay special attention to these findings in order to reduce the students’ uncertainties in this particular semester.

Furthermore, Asian students have the highest proportion in mastery-intrinsic orientation. European students are the second and then come the Finnish students. Figure 1 presents the pattern of the male and female mastery-intrinsic orientation level.

Male and female pattern differences as for mastery-intrinsic orientation
Figure 1. Male and female pattern differences as for mastery-intrinsic orientation
  1. What approach Haaga-Helia UAS needs to take to anticipate dropouts?

Based on our findings, BITE degree programme must pay special attention when they have the following indicators:

  • Students who complete assignments inattentively or even do not complete assignments at all.
  • Students who do not often participate in the lectures.
  • Third-semester students need special support and attention.
  • Seventh semester students need attention to finalize their studies.

5 Actions and recommendations for the BITE programme

For Freshmen, we have designed and developed an Augmented Reality APP that helps new BITE students to learn about the Haaga-Helia Pasila campus premises such as the library, classes, key people, academic advisor, BITE study paths (Software Engineering and ICT infrastructure), and tools (Mynet, HHmoodle, and Asio). The aim of this app is to reduce new students’ fear-of-failure by making the environment and the context familiar for them. Please see picture 1.

Sample screenshots of the Freshman application
Picture 1. Sample screenshots of the Freshman application.

Furthermore, based on the findings in this study, the BITE programme has designed and developed a proof of concept, the Adaptive Management System (AMS) platform, that anticipates and prevents students’ dropouts (Dirin & Laine 2018). The AMS platform will extract data directly from school systems such as HHmoodle and Winhawille, and embed data mining algorithms to understand each student’s academic needs. It is an individualized learning assistant, and an academic progress-tracking tool. Specifically, the platform provides students with self-reported evaluations at the beginning and the end of each semester observing students’ motivational transitions. More details about the AMS will be published in an article in e-Signals in May.

For future study, Bayesian machine learning algorithm will be applied to predict the future trends in BITE degree programme, and to define how to affect avoidance orientation and academic withdrawal based on deep learning analysis.

Special thanks goes to Ling Sun for conducting the Thesis and Riitta Blomster for proof readings.

 

References

Dirin, A., & Laine, T. H. 2018. Towards an Adaptive Study Management Platform: Freedom through Personalization. In CSEDU.

Niemivirta, M. 2002. Motivation and performance in context: the influence of goal orientations and instructional setting on situational appraisals and task performance. Psychologia – An International Journal of Psychology in the Orient, 45(4), 250–270. https://doi.org/10.2117/psysoc.2002.250.

 

The author of this article Amir Dirin is a Principal Lecturer at Haaga-Helia UAS.

E-Signals tarjoaa sisältöjä Haaga-Helian osaamisalueilta kiinnostavasti ja vaikuttavasti. Onko mielessäsi juttuidea? Ota yhteyttä julkaisut@haaga-helia.fi.

Haaga-Helia Julkaisutoiminta

E-Signals tarjoaa sisältöjä Haaga-Helian osaamisalueilta kiinnostavasti ja vaikuttavasti. Onko mielessäsi juttuidea? Ota yhteyttä julkaisut@haaga-helia.fi.

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