Performance and progression of first year ICT students
Judy Sheard, Angela Carbone, Selby Markham, A J Hurst, Des Casey, Chris Avram
Caulfield School of Information Technology Faculty of Information Technology
Monash University
PO Box 197, Caulfield East, VIC, 3145, Australia
judy.sheard{angela.carbone, selby.markham, ajh, des.casey,chris.avram}@infotech.monash.edu.au
Abstract
1In 2006 the Computing Education Research Group from Monash University conducted a study that explored the perceptions students bring into ICT degrees and the perceptions that staff have of student expectations. Students and staff from first year undergraduate ICT degrees on the five Victorian campuses of Monash University participated in a series of data collections. The research was conducted using a mixed quantitative and qualitative research design. This paper reports on that aspect of the study that investigated influences on students’ progression and performance in the first year of their undergraduate ICT degrees. This study reports the following three findings: it is difficult to predict whether students will complete a unit from their interests and expectations of the degree; students with prior knowledge of programming or who had English as a first language or who had entered the degree from high school received higher results in programming; females are more likely to drop out of technical units than are males.
Keywords: first year ICT students’ performance and progression.
1 Introduction
Of critical concern in tertiary institutions in Australia, as in many other countries, is the recent severe drop in numbers of students applying for entry into ICT degrees. This drop, combined with poor performance, progression and retention within ICT programmes, has seen the ICT discipline in a crisis. In an attempt to redress the drop in student numbers, many universities have lowered their entry scores into ICT degrees. However, demand for ICT degrees remains low.
Students who undertake tertiary ICT study enter their courses with a range of skills and experiences. These help shape their perceptions of their chosen course and it seems reasonable to expect that these would influence their performance and progression at university.
Copyright © 2008, Australian Computer Society, Inc. This paper appeared at the Tenth Australasian Computing Education Conference (ACE2008), Wollongong, Australia, January 2008. Conferences in Research and Practice in Information Technology, (CRPIT), Vol. 78. Simon and Margaret Hamilton, Eds. Reproduction for academic, not-for profit purposes permitted provided this text is included.
A recent Australian study by Multimedia Victoria (Multimedia Victoria, 2007) investigated perceptions of 14-19 year olds towards ICT study and careers. This study showed that there is interest in ICT at the secondary school level. The study found that almost half of students surveyed showed some interest in ICT and 13% showed ‘strong interest’. Students who had exposure to ICT at secondary school (particularly in Years 9 and 10) were more likely to be interested in further study and/or a career in ICT. The study presented in this paper aims to gain an understanding of interests, expectations and skills of students entering ICT degrees and the influence of these factors on their performance and progression in their degree programmes.
This study was motivated by the restructuring of an ICT faculty’s undergraduate offerings, and a desire to investigate students’ responses to the new degrees. It was recognized that the new offerings could affect student demand, and it was important to gain insights into the perceptions and experiences of students undertaking the new program. Furthermore, it was felt that by identifying factors that influence academic performance and progression, intervention programs might be designed and targeted to support those at risk of failure or discontinuing their courses.
This paper is organized into six sections. Section 2 presents an overview of previous research work conducted on the issue of tertiary student progression and performance, with an emphasis on studies of ICT in Australia. Section 3 describes the context of the study, the study design and data collection methods. Section 4 reports on the findings of student performance and progression. This is followed by a discussion of the results in Section 5, together with concluding remarks and directions for further research.
2 Background
There has been extensive research on the issue of student performance and progression in the Australian tertiary education sector (Dickson et al., 2000; Marks et al., 2001; McKenzie & Schweitzer, 2001; McMillan, 2005). This research has been conducted across a range of disciplines and has mainly investigated factors that can predict academic performance and retention rates. Studies by James (2000), Kantanis (2000), McMillan (2005), Peel (1998) and Taylor (2004) are examples of such research. These investigations found a range of factors have affected student performance and retention rates. For example, the study by James found that students who are
uncertain about their choice of career are less likely to succeed with their course and more likely to withdraw. The results of these studies support the findings of similar investigations conducted in countries other than Australia, such as those reported by Glynn (2006), Milem (1997) and Tinto (1993).
Another body of research has investigated student performance and progression within the ICT discipline. The focus of most of these studies has been student performance in programming. Such studies have attributed programming success to factors relating to the individual, for example: learning styles and problem-solving skills (Goold & Rimmer, 2000; Haden, 2006); prior academic experience (Simon, Fincher et al., 2006); spatial visualisation skills and map drawing styles (Simon, Cutts et al., 2006; Tolhurst et al., 2006). These findings have been supported by other research: for example, studies by Beaubouef (2001) and Bergin (2005). Other international studies have attributed programming success to the student’s background in mathematics and science (Bergin & Reilly, 2005; Byrne & Lyons, 2001; Wilson & Shrock, 2001). However, the most frequently mentioned factor for success in programming is previous programming experience (Bunderson & Christensen, 1995; Byrne & Lyons, 2001; Hagan & Markham, 2000; Ramalingam et al., 2004; H. Taylor & Mounfield, 1994; Wilson & Shrock, 2001).
A particular area of concern is the performance and retention rates for female students studying ICT. A recent study by Lang (2007) reported on gender differentiation in failure rates of ICT students in Australia. She found that the highest failure rates of females are in the technical subjects. This, combined with the dramatic decreases in percentages of females in ICT degrees over the last decade (Bunderson and Christensen (1995), has meant that there are low and decreasing percentages of women entering the ICT workforce (Prabu, 2007).
This study provides insights into the perceptions of students that enter undergraduate ICT degrees at Monash University and the influence of these on their performance. This study takes a broad approach in that it was carried out in four undergraduate degrees, the entire undergraduate offerings for the Faculty of IT. The study also considered the students’ interests in individual topics within ICT units. The demographics of students studying these units are reported, along with secondary school attainment scores, prior experience with ICT, prior programming knowledge, course preference and reasons for choice, perceived problems relating to learning in the university environment by the students and interests and expectations of their degrees. The influence of all these factors on student performance and retention are investigated.
3 Data Collection
This section reports on the context and design of the study and the participants.
3.1 Context of Study
The context of this research was four undergraduate ICT degrees at five of the Victorian campuses of Monash University. The undergraduate degrees are:
• Bachelor of Information Technology and Systems. A three-year general ICT degree programme that prepares future information technology professionals for careers in a range of specialised information technology fields including: applications development and networks, business systems, multimedia, net-centric computing, systems development, information systems.
• Bachelor of Business Information Systems. A three-year programme in business information technology. It prepares students for leadership roles in ICT management and focuses on the application of information technology to the solution of business problems.
• Bachelor of Computer Science. A three year programme which provides an in-depth study of computing with an emphasis on the software, hardware and theory of computation to solve commercial, scientific and technical problems. • Bachelor of Software Engineering. A four year
programme which explores the design, construction and engineering of large software systems, subject to constraints such as cost, time and risk management. In the first semester of these degrees there are three units that are core (common) across all the programmes: Computer Systems, Computer Programming, and IT in Organisations.
3.2 Project design
The data collections were conducted in first semester of 2006. Data were gathered using the following methods: • a start of semester survey of students, using a
structured questionnaire;
• the final results in the three core units for students who agreed to be identifiable;
• mid-semester interviews of students and staff. The methodological basis for the research design was derived from the assumption that little knowledge was known of students’ backgrounds and expectations of their degrees, and how these factors related to their performance and experiences in their degrees. It was decided that an exploratory survey and follow-up analysis of end-of-semester results would provide an indication of the factors influencing performance and progression. The interviews of students and staff would provide understanding of the experiences and adaptation of students to university life. This aspect of the research will be reported at a later date (Sheard & Carbone, 2007). Several groupings of students are mentioned in the reporting of the research in this paper. The students that responded to the survey are referred to as respondents;
the students that agreed to allow their results to be analysed are referred to as participants.
The project was prepared for ethics approval and the Monash University Standing Committee on Ethics in Research involving Humans (SCERH) allowed all aspects of the project data collection.
The remainder of this section describes the materials, methods, analysis and reporting used for the survey and data collection.
3.3 Survey of students
At the beginning of first semester 2006 a paper-based questionnaire was issued to students enrolled in the unit Computer Systems. This unit was chosen as it is taken by the majority of first year students enrolled in the ICT degrees and there are only a small number of students who gain exemption from this unit.
In first semester 2006 there were 422 students enrolled in Computer Systems. A total of 185 students responded to the survey, giving a response rate of 44 %.
The survey questionnaires were distributed to the students by a project team member at the start of the Computer Systems lecture at each campus. Most of the data was collected during the first and second week of the semester.
3.3.1 Survey questionnaires
The survey questionnaire was designed by members of the project team and trialled with teaching staff and students. A copy of the questionnaire may be found at: http://cerg.infotech.monash.edu.au. The questionnaire contained mainly closed response style questions with one open response question. The items in the questionnaire represented a number of different behavioural domains including:
• basic demographical data (11 questions);
• formal and informal experiences with ICT prior to entry into their degree (3 questions);
• reasons for doing the degree (3 questions);
• expectations of the degree (5 questions, including open-ended response question );
• expectations of the university experience (6 questions);
• expected topics to be covered in the degree (26 topics listed in Table 4);
• interest in aspects of ICT (26 topics listed in Table 4);
The topics for the items for the ‘Expected topics to be covered in the degree’ and ‘Interest in aspects of ICT’ questions were aligned where possible to the list of topics
from the 2005 ACM/AIS/IEEE computing curriculum2. This curriculum presents a list of topics covered in undergraduate degree programmes in five major computing disciplines. The complete list has 40 computer related and 17 non-computer related topics. A subset of these topics was used in the questionnaire. In considering which topics to include, only topics that were covered in the FIT undergraduate degrees were used. Several topics were consolidated under one topic and extra topics were added which were covered in the FIT degrees but not explicitly mentioned in the ACM list. The final list contained 26 topics, from the original 57, a more reasonable number for students to address.
3.4 Student results
The end of first semester results for the three core units Computer Systems, Computer Programming and IT in Organisations were collected for all students. Analysis of results for the students who had agreed to participate (participants) was used to explore student performance and progression.
3.5 Data analysis and reporting
The survey responses of students were analysed to provide descriptive information and a variety of statistical tests were performed on this data. Descriptive and statistical analysis of the quantitative data was performed using SPSS. For all statistical tests conducted, a level of p < 0.05 was used to determine statistical significance. The overall survey response rate was 44% (respondents) and an overall 28% of the student cohort agreed to allow their results to be analysed (participants). Therefore, in presenting the results of analyses, the numbers of students used varies depending on which group is used: respondents or participants. Across the four degrees, the survey response rates varied from 32% to 71% and the willingness to participate varied from 23% to 48%. . 4 Research findings
4.1 Demographics
The survey data were analysed to provide a profile of the respondents. A number of these items were then used as independent variables in the analysis of results in sections 4.2 and 4.3.
The respondents were aged from 17 to 34 years, with a median age of 20. The ratio of male/female students was 77/23. English was the first language of 66% of the respondents; 20% of respondents were international full-fee paying students.
Most students (96%) were studying in fulltime mode. Nearly all respondents had access to a computer (98%). Most respondents had their own computer (87%) rather than a shared computer and 10% of the respondents brought a laptop to university.
2
ACM/AIS/IEEE-CS Joint Task Force on Computing Curricula 2005: The Overview Report
4.1.1 Pathway into the degree
A majority of respondents (65%) had entered their degrees directly from school. The median ENTER3 score of the respondents who had completed VCE4 was 81.0 and the mean was similar at 81.7 with a SD of 10.8. The ENTER score is a measure based on a student's top four VCE study scores (including English) and 10% of their next two best study scores, which is then ranked against the aggregate scores of other students. For example, an ENTER score of 81 indicates that a student has achieved a higher ENTER than 81% of the student cohort.
4.1.2 Prior experience with ICT
Most of the respondents (86%) indicated that this was their first attempt at a tertiary ICT degree. More than two thirds of the respondents (68%) had studied ICT at school. This was usually in the form of VCE units in the last two years of secondary school study. However, some respondents had studied the VET Certificate in Information Technology5 (11%).
4.1.3 Prior knowledge of programming
Most respondents (84%) indicated that they had some knowledge of a programming language. However, only 41% respondents had previously formally studied a programming language. The most widely experienced language was Basic/VB (60%), and a considerable portion of students had experience with HTML (62%). Almost a third of the respondents had some experience with Java (30%), the language of instruction for the core programming unit in these undergraduate degrees. A few had experienced Pascal/Delphi (5%) or CGI/Perl (4%).
4.1.4 Degree preferences
Most respondents (82%) claimed that the degree they were studying was their first preference within the ICT degree options they had selected, with 68% claiming that a Monash ICT course was their first preference overall. Students were asked to select up to four reasons why they had selected their degree from a list of set responses. From Table 1 it can be seen that the pattern of responses the students nominated is clear, with the most popular reasons concerned with having an interest in ICT and seeing the degrees as providing a good pathway into a career in ICT. This finding is in agreement with the Greening (1998) study that showed that personal interest and skills development were the most frequently stated reasons for doing a computer science degree and also with the Reality Bytes (2001) research, which showed that interesting and satisfying work was the most desirable job characteristic.
3
Equivalent National Tertiary Entrance Rank. This is the national Australian tertiary entrance score.
4
Victorian Certificate of Education 5
A vocational training programme in ICT.
Although a number of studies have shown that students claim their parents, teachers and friends are the most important influences in their degree decision making (e.g., Reality Bytes, 2001), this study shows that students are making decisions based on personal preferences rather than on the suggestions of others.
Table 1 Respondents’ reasons for doing their degree
Main reasons for doing the degree N % of
total group
Interest in ICT 146 79
Good degree for getting into an ICT career 109 59 An interesting and challenging degree 86 47 Extend my knowledge and skills 66 35 Reputation of this Monash degree 42 23
To earn good money 38 21
Best I could get with my ENTER score 26 14
Other reason 15 8
My parents wanted me to do this type of
degree 14 8
No clear reason 11 6
My friends were doing this type of degree 9 5
Suggested by a teacher 7 4
4.1.5 Perceived problems
The students were asked to select up to four main problems that they saw themselves facing during the year. Their responses are shown in Table 2. It can be seen that the principal perceived problems in entering the university were related to uncertainty about the social and teaching environments.
Table 2 Problems respondents’ perceive they will face
Problem N % of
total group Not knowing what is expected of me by
lecturers and tutors
78 42 Not studied at university level and I am
unsure what is expected
74 40 Making social contacts 41 22 Expressing myself in writing 33 18 Having enough money to live on 32 17 Expressing myself in groups 32 17 Not having enough money for books 30 16 Having to travel a long way to attend
lectures
20 11 Not having a place to study properly 15 8
Other problem 13 7
4.1.6 Expectations of the learning environment The students were asked to rate how well they felt that they would learn in various situations. A 5-point Likert scale was used where 1 indicated not at all and 5 indicated very well. The means and standard deviations of these ratings are shown in Table 3. It is clear that at the beginning of their courses the students felt comfortable with the range of learning options that they would experience in their course, even though they might have had minimal experience of some of them. They showed a preference for small interactive classes rather than lectures or working unsupervised.
Table 3 Mean ratings of how well respondents’ felt they learn in different situation
Learning situation N Mean SD
Computer lab classes working
on a computer on my own
177 3.98 0.84 Tutorials where I am
expected to participate 170 3.87 0.83 Classes where I have to
work in a small group 175 3.83 0.86 Tutorial classes devoted
to providing answers to problems
173 3.68 0.98 Outside class on my own 180 3.67 0.87 Outside class with friends 175 3.45 0.91 Lectures/formal classes 180 3.44 0.78 Likert scale responses, where 1= not at all and 5 = very well
4.1.7 Interests and Expectations
In a section of the questionnaire the students were presented with a list of 26 topics and asked to indicate how much of each topic they expected to learn in their degree. A 5-point Likert scale was used where 1 indicated nothing and 5 indicated a great deal. The students were then presented with the same list of topics and asked to indicate their level of interest in each topic. A 5-point Likert scale was used where 1 indicates nothing and 5 indicates a great deal. The mean and standard deviations of the ratings for these responses are shown in Table 4. The ‘expect to learn’ and ‘interest’ ratings were compared to determine any alignment or mismatch in what the students expected to learn and were interested in. The means of the ratings were compared using correlated means t-tests6. Areas of alignment are shown by non-significant results and areas of mismatch by non-significant results. The results are shown in Table 4.
Considering the cases of mismatch, there are 15 significant results as indicated by the asterisks in Table 4. Five cases occurred where the students’ level of interest
6
A statistical test for the difference between the means of two related measures taken on the same individual.
was greater than their expectations of what they would learn, as indicated by the negative t-test values (see Table 4). These were topics which could be seen as appealing in nature (e.g. writing computer games, computer graphics and animation) or state-of-the-art (e.g. developing applications for mobile phones). These findings align with the Multimedia study (Multimedia Victoria, 2007) which found that students expressed highest interest in careers as ‘games developer’ and ‘graphic designer’. The interest in these areas indicates topics that might be of interest to include in a course.
The ten other cases of mismatch occurred where the students’ expectations of what they would learn were greater than their interest, as indicated by the positive t-test values (see Table 4). These topics tended to be non-technical (e.g. information systems or project management) or theoretical in nature (e.g. theory of programming languages).
The means of the ratings of interest and expectations were compared based on gender. These comparisons showed more alignment in expectations and interest for females than males. There were only six topics for females which indicated mismatch in expectation and interest; however, for males, there were fourteen topics indicating mismatches.
4.2 Performance
The end of semester results for the core units Computer Systems, Computer Programming and IT in Organisations were used to investigate factors influencing student performance in the degrees.
The numbers of participants who had an end of semester result recorded and the overall numbers of results recorded for each core unit are shown in Table 5. To establish if the participants could be seen as representative of the cohort in each core unit, cross tabulations of the pattern of grades for the participants and the total group were performed. These showed that there were no significant differences in the pattern of results in Computer Systems and IT in Organisations. In Computer Programming the participants performed better than the cohort.
Table 4 Descriptives for Expect to Learn and Interest items
Expect to learn topic Interested in topic
Topic
N Mean SD N Mean SD t-test
Building databases e.g. Access 162 3.33 0.97 168 2.92 1.16 4.08* Communication and presentation skills 169 3.27 0.93 171 3.25 1.01 -0.30 Computer graphics and animations 173 2.83 1.21 171 3.55 1.20 -8.06*.
Computer networks 172 3.60 0.90 172 3.77 1.07 -2.25*
Developing computer systems for businesses
applications 173 3.46 0.98 172 3.34 1.20 0.84
Developing computer systems for
scientific/engineering applications 170 3.08 1.05 171 2.99 1.24 0.70 Developing computer systems for the World
Wide Web (Web) 172 3.58 0.90 172 3.63 1.07 -0.67
Developing computer systems for use in
devices such as mobile phones 172 2.80 1.14 171 3.07 1.24 -2.87* Digital logic and electronic circuit design 168 2.94 1.15 167 2.83 1.21 0.84
How a computer works 175 4.05 0.86 173 3.58 1.13 4.56*
How operating systems work e.g. Windows,
Linux 173 3.83 0.93 173 3.73 1.11 0.64
How people interact with computers (HCI) 176 3.64 0.90 174 3.27 1.09 3.56* How to build computer controlled robots 163 2.47 1.17 168 2.86 1.32 -3.51*
How to write programs 175 4.11 0.97 171 3.84 1.17 2.95*
Information management 174 3.66 0.82 172 3.30 1.07 3.68* Information systems analysis and design 174 3.77 0.84 170 3.31 1.14 5.05* Legal / Professional / Ethical issues in
computing 168 3.15 0.96 169 2.72 1.23 3.87*
Mathematics 172 3.05 1.18 171 2.81 1.34 1.80
Project Management 171 3.50 0.94 172 3.21 1.09 2.70*
Report writing and program documentation 172 3.53 0.94 171 3.09 1.10 5.56* Security issues and management e.g. computer
viruses 173 3.46 0.89 173 3.59 1.02 -1.87
Software design 174 3.69 0.98 172 3.81 1.02 -1.46
Solving computer problems 174 3.91 0.88 174 3.89 1.01 0.7 Testing computer programs and systems e.g.
finding bugs 171 3.78 0.96 173 3.60 1.07 1.93
Theory of programming languages 174 3.80 0.92 172 3.14 1.21 6.54* Writing computer games 170 2.69 1.22 172 3.31 1.38 -6.43* * indicates t-test significant a p<0.05
Table 5 Numbers of students with final results recorded for Computer Systems
Unit Total number of results Participants with results % of group Computer Systems 330 67 20 Computer Programming 321 51 16 IT in Organisations 265 48 18
To see if there was any relationship between ENTER score and performance, ENTER scores were correlated against final results using Pearson’s Correlations. No relationships were found.
Comparisons of the mean results of the participants grouped according to the independent variables of gender, English as a first language, degree entry, prior study of ICT and prior knowledge of programming found few differences. There were no differences found for Computer Systems and IT in Organisations. Differences were found for Computer Programming results for three variables: English as a first language, entry pathway and prior knowledge of programming. The details of these are reported in the following three sections.
4.2.1 Relationship between English as first language and unit results
The mean results for each core unit were compared according to whether English was the first language of the students. This showed that the students with English as a first language achieved higher results in Computer Programming (M = 82.48, sd = 14.95) than those with another first language (M = 66.82, sd = 26.44) and t-tests showed that this difference was significant (t(54) = 2.76, p < 0.05)7.
4.2.2 Relationship between entry pathway and unit results
The mean results for each core unit were compared according to whether the students had entered their degree from secondary school. This showed that the students who had entered their degrees from school achieved higher results in Computer Programming (M = 85.52, sd = 14.05) than those who had had other pathways (M = 71.35, sd = 23.46) and t-tests showed that this difference was significant (t(54) = 2.30, p < 0.05).
7
APA standard for reporting of a t-test showing degrees of freedom and t statistic.
4.2.3 Relationship between prior knowledge of programming and unit results
The participants were grouped according to whether they had any prior knowledge of programming when entering the degree. Comparison of the mean results of these groups showed that the students who had prior knowledge of programming achieved higher results in Computer Programming (M = 78.34, sd = 24.21) than those who had no prior knowledge (M = 61.25, sd = 20.66). (t(54) = 2.11, p < 0.05).
4.2.4 Relationship between interest ratings and unit results
The relationship between interest ratings and unit results was tested using Pearson’s Correlations. The most noteworthy data from this aspect of the analysis was that the “Interest” items did not have any statistically significant correlations with unit performance. Few measures of interest tended to be related to actual performance. This is a complex area on which researchers in this area appear reluctant to focus (Bright et al., 2005). 4.3 Progression
An area of interest in this study was the retention rates of students in the core units.
There were 67 students who had a result recorded for Computer Systems out of the original 119 who agreed to be identifiable. This gave a retention rate (rate of getting to an assessable result) of 56%. The retention rate for the other students in this unit was 62% (266 of 427).
Comparisons of the Computer Systems retention rates of participants grouped according to gender showed that males were less likely to drop out of the unit than females and a chi-square test showed this difference was significant (χ2 (1, N = 117) = 8.18, p < 0.05). A similar result was shown for Computer Programming. However, no difference based on gender was found for IT in Organisations
Comparisons of other independent variables of English as a first language, entry pathway, prior study of ICT and prior knowledge of programming found no differences in retention rates for the three core units.
To investigate possible factors in retention, the reasons that participants gave for doing their degree, the problems that they perceived they would face in doing their degree and the students’ preferences for learning situations were analysed. The retention rates in Computer Systems were explored as this was the unit in which the survey was conducted. Cross tabulations were used to find any differences in the responses to these questions between the participants who had a final grade recorded for Computer Systems and those who did not.
Of the reasons for doing the degree, there were three reasons that produced statistically significant differences. First, the students who did not have a final result recorded for Computer Systems were more likely to have said that they had chosen their degree to extend their
knowledge and skills (χ2 (1, N = 119) = 7.69, p < 0.01)8. Second, the students who did not have a final result recorded were more likely to have said that they had chosen their degree because it appeared to be an interesting and challenging degree (χ2 (1, N = 119) = 4.89, p < 0.01). Third, the students that had a final result recorded were more likely to have said that they had no clear reason for choosing their degree (χ2 (1, N = 119) = 4.05, p < 0.01). These results whilst statistically significant are counter-intuitive as there seems to be no discernable reasons why these factors should be causally linked.
Students who nominated ‘not knowing what is expected of me by lecturers and tutors’ were more likely to have dropped out of the Computer Systems unit (χ2 (1, N = 119) = 3.72, p < 0.01).
The students’ preferences for learning situations produced no differences in retention. This may be a result of the students producing the ratings from poor information, although at the time of the survey they had had some experience of some of these learning situations. 5 Discussion, conclusion and future work The intention in carrying out this research was to explore the perceptions of ICT that students bring into their ICT degrees, and investigate how these might influence performance and retention with ICT courses. The data suggests that the ICT experiences and knowledge that students brought into their degrees had little influence on their performance and progression at university.
It was assumed at the start of this study that there would be some relationship between interest, expectation and performance. In the final analysis, there were minimal relationships with performance that were significant. The ‘interest’ items did not show any significant correlations with performance. The ‘expect to learn’ items had a few significant correlations with performance, for example there were relationships between performance in Computer Programming and the following ‘expect to learn’ items: HCI, how to write programs, solving computer problems and testing computer programs. A summary of the main findings reported are:
1. students with prior knowledge of programming, who had English as a first language or who had entered the course directly from school were more likely to succeed in computer programming;
2. students’ interests and expectations of the degrees are not good predictors of whether students will complete a unit of study;
3. students with unclear expectations are more likely to drop out; and
8
APA standard for reporting of a chi-square test showing degrees of freedom, sample size and chi-square statistic.
4. females were more likely to drop out ofthe more technical units (for example, computer systems and computer programming).
These findings emphasise the need to research these areas in more detail.
An area of future research would be to look at the reasons why students fail and/or withdraw from units and courses. This could be done effectively through exit style interviews. Such interviews would provide data on what went wrong for students and what would help staff better understand the outcomes of their teaching.
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