1. Transition, Engagement and Retention of First Year Computing Students Heather Sayers Mairin Nicell Anne Hinds

2. Outline  Purpose of the study  Transition and Retention in School of Computing and Intelligent Systems  The Experiment  Data Analysis and Results  Conclusion

3. Purpose of the Study  Create a full profile of the 2009-10 first year student cohort’s educational and social backgrounds  Continuously monitor student engagement and academic progress throughout semester 1 from an ‘inside’ perspective  Provide a variety of opportunities for students to provide feedback on the first year experience  Analyse the data gathered in relation to transition and retention

4. Transition and Retention in SCIS  First Year Teaching team  Attendance monitoring and follow-up; weekly meetings to consider students at risk and to take speedy action when necessary  Small group weekly tutorials  Extended induction  Social induction

5. The Experiment  Semester 1, 2009-10  106 participants (79 male and 27 female)  Initial questionnaire (educational and social backgrounds)  Focus groups  Informal interviews  The “Inside” perspective – RA in lectures and practicals

6. Data Analysis and Results  Statistics on student attendance and semester 1 performance were added to the data.  Transition, retention and engagement issues were considered under the following headings: Attendance Employment Educational Background Subject-specific issues Teaching Delivery Socialisation

7. Attendance  Hypothesis: poor attendance = poor performance.  ANOVA: Independent variable: overall attendance (0-20% 21-40% etc.); Dependent variable: overall semester 1 average.  Attendance was found to have a significant effect on performance, with poor attendance relating to poor performance (p=0.000, F[3,61] = 12.208).  29% of the 106 participants attended 81-100% of classes.  >50% of the IFY students (22) were in this higher attendance category compared to 20% of the year 1 students.

8. Attendance  Of the year 1 modules, Mathematics, Programming and Computer Games had the highest recorded attendances.  Modules with higher levels of continuous assessment (Maths and Programming) or smaller class sizes (IFY modules and Year 1 Computer Games) had higher rates of attendance and better performances.  Difficult to pinpoint a particular reason why students choose not to attend.  RA convinced that motivation and support are the key factors.

9. Employment  The hypothesis that the more hours worked, the lower the students’ attendance and performance would be, was tested.  40% in part-time employment (34 Year 1 students and 8 IFY students) with 70% of these working 10-20 hours per week.  Employment was found not to have a significant effect on either attendance or performance (p = 0.512, F[1, 65] = 0.434).

10. Educational Background  The hypothesis that grammar schools entrants would achieve better results was tested.  ANOVA: Independent variable: type of secondary school; Dependent variable: overall average.  ANOVA: Independent variable: type of qualification; Dependent variable: overall average.  Type of secondary level school attended was found not to have a significant effect on first semester performance (p=0.185, F[1,68] =1.792).  No significant difference was found for entrance qualification also.

11. Subject-Specific Issues  SCIS course provision: an Integrated Foundation Year; 4 single-honours Computing degrees; several combined degrees with other disciplines.  National Audit Office report (2007): highest non- continuation figures.  55 single honours students compared with 29 combined degree students in Year 1.  Higher percentage of combined degree students failed the semester with an overall average of <40% (18% vs 12%) and none reached the highest or lowest performance categories (80-100% and 0-20%).

12. Teaching Delivery  Even 2-hour lectures were considered too long, and the general consensus was for more practical/tutorial classes instead.  Students liked: “the freedom”; being “treated like an adult”; and “the more relaxed atmosphere” as opposed to being “constantly told what to do next” (at school).  One student summed it up saying “I don’t really learn that well in the lectures, just being talked to, rather than doing something like in a practical”.  But this change of environment can be seen as “a lot of cord to hang yourself” with!

13. Socialisation  62% of participants had friends also attending the Magee Campus, with half of these on the same course.  Almost two-thirds live at home and travel.  Feedback from focus groups – not enough opportunities to mix with students and staff.  One outing in semester 1 – good feedback.  Further funding obtained for 2010/11 and events planned.

14. Challenges  The results from this study have challenged us to consider in further detail: The way we deliver modules; The level and type of support we provide; The level of attendance monitoring; The level of social integration.

15. Conclusion  Some level of attrition cannot be avoided.  Poor attendance continues to be a major contributing factor to poor attrition.  Student motivation and student support are key factors.  There are multiple factors to consider within each individual student cohort.  Initiatives need to be adapted dynamically to suit identified needs, and to suit the subject area.  No one solution fits all.  Keep trying!