Faculty of Social and Behavioural Sciences
Graduate School of Childhood Development and Education
Matching Students and Degree Programs
The Contribution of a Pre-Entry Matching Week to Prospective University
Students’ Understanding of a Degree Program and their Study Choice
J. E. Schijf, BSc (5828309)
Thesis 2 Research Master Child Development and Education Faculty of Social and Behavioral Sciences
University of Amsterdam
Supervisor: prof. dr. S. E. Severiens Second reader: prof. dr. M. L. L. Volman Third reader: dr. A. Zand Scholten
Abstract
Research has shown that correct expectations about studying in university education contribute to a successful transition from secondary education into university education. Prospective students often do not know what to expect of studying in university education and after entering university they experience a gap between their expectations and experiences. The current paper reports the results of two studies. Study 1 explored whether a pre-entry intervention, called Matching Week, helped prospective students to get a better understanding of a degree program, complemented other study choice activities, and whether such a week was helpful in their study choice. Prospective first-year students (N = 2272) filled out a questionnaire on their experiences during the Matching Week. The
Matching Week helped students to get a better understanding of a degree program and complemented
other study choice activities, but had no influence on their study choice. Multilevel analyses indicated that the more effort students invested in the week, the better their understanding of a degree program. Moreover, prospective students who gained a better understanding of a degree program found the week more useful for their study choice. Study 2 aimed to investigate which program factors contributed to the success of a Matching Week, being defined as giving better insights in several degree program aspects. Interviews with stakeholders (N = 9) indicated that creating a
non-anonymous learning environment via seminars, assignments and group work seemed to be essential to ensure that prospective students put effort in the week. This was important, since previous analyses indicated that effort predicted whether the Matching Week helped student to get a better understanding of a degree program. Moreover, it was important to offer a wide range of teaching methods,
assignments and assessment methods, which led to more in-depth study behavior and to more exposure to degree program characteristics. Suggestions for further research include longitudinal monitoring of the Matching Week and the evaluation of different formats of the week in experimental settings.
Keywords: higher education, transition, withdrawal, study choice, intervention
Introduction
For many years, researchers have examined which factors contribute to student attrition in the first year of university education (e.g. Harvey, Drew & Smith, 2006; Tinto, 1994; Tinto, 2006; Zepke & Leach, 2005). Many factors are associated with students’ decision to withdrawal, varying from student characteristics such as age, social class and gender (e.g. Bruinsma & Jansen, 2009; Van den Berg & Hofman, 2005), to external pressure from for instance family or work (e.g. McKenzie & Schweiter, 2001; Willcoxson, 2010), and institutional characteristics, including academic support and student-staff interaction (e.g. Briggs, Clark, & Hall, 2012; Hurtado et al., 2007; Torenbeek, Jansen, & Hofman, 2010). Despite these insights, student retention rates are still a matter of concern. In the
Netherlands, approximately 35-40% of the first-year students in university education leave their programs during their first year (Dutch Inspectorate of Education, 2014). Most of these students start a year later in a different degree program, while each year 10% of the first-year students withdraws without coming back to a university program.
In 2013, the Dutch Ministry of Education signed the ‘Quality within Diversity’ act (in Dutch: ‘Kwaliteit in Verscheidenheid’) aimed at, amongst other things, decreasing attrition rates and
improving student success in higher education (Act Quality within Diversity, 2013). This act resulted in the introduction of a so-called ‘pre-entry study check’ with implications for both prospective students and higher education institutes. All prospective students have the right to an opportunity to check their study choice, for example via a meeting with a counselor, taster sessions or shadowing. At the same time, universities have the right to obligate students to participate in such a study check activity, such as filling out a compulsory questionnaire to uncover at risk students or following a pre-entry lecture. The purpose of the pre-pre-entry study check is to raise awareness among prospective students regarding the motives of their study choice and enabling them to verify whether their expectations of studying in higher education meet reality.
Expectations about studying in higher education
Correct expectations about student life are a key factor contributing to student success and student retention in university education (e.g. Jackson, Pancer, Patt, & Hunsberger, 2000;Haggis, 2006; Hultberg et al., 2008). Nevertheless, research shows clear gaps between expectations and experiences in higher education (Lowe & Cooke, 2003; Long & Tricker, 2004; Thomas, 2011; Thomas, 2013). This could be problematic: a mismatch between students’ expectations and their experiences is a commonly given reason for withdrawal in the first year (Rowley, Hartley, & Larkin, 2008). In addition this mismatch refrains students from adjusting to and integrating in the university, which are also risk factors for early leaving (Lowe & Cook, 2003; Harvey et al., 2006; Jones, 2008). Thus, reducing the gap between expectations and experiences is associated with a positive effect on retention rates. A study on first-year students’ expectations about university education shows for example that students with more in depth and complex expectations fit better in their new learning environment than students with more basic ideas (Pancer, Hunsberger, Pratt, & Alisat, 2000).
There are several theories about the origin of students’ misconceptions. Research conducted in the UK and Australia indicates that students feel poorly informed about studying in higher
education (McInnis, James, & Hartley, 2000; Krause, Hartley, James, & McInnis, 2005; Harvey et al., 2006). In particular, they experience a lack of information about the course content, workload and the time they are expected to spend on their studies (McInnis et al., 2000). Students frequently criticize universities for misleading information about degree programs (e.g. Harvey et al., 2006; Yorke, 2000). They say that promotion does not represent the degree program in a realistic way and as a
result, they are not capable to make well-informed decisions. This could thus result in inadequate study choices, and ultimately withdrawing and high attrition rates.
Moreover, it seems to be difficult for students to identify differences between secondary school and university education before the transition. A survey research among prospective first-year students indicated that they know university education will differ from secondary education, but they were not able to explain these differences in detail (Crisp et al., 2009; Rowley et al., 2008).
Prospective students expected, for example, that they had to become more independent and autonomous learners, but after entering university they frequently criticized teachers for a lack of guidance (Bates & Kaye, 2014; Leese, 2010). In conclusion, it seems to be important to help prospective students to get a realistic understanding of university education before they enter university in order to support a successful transition (Crisp et al., 2009; Rowley et al., 2008).
Success factors that may improve the transition
A literature search uncovered only a few research studies on interventions aimed at shaping correct student expectations (Murtagh, 2012; Thomas, 2011; Thomas, 2013). Most of these
interventions focused on target groups, such as prospective students from lower socio-economic groups, ethnic minority students and students who are for other reasons at risk for withdrawal. Nevertheless, these studies have indicated possible success factors.
An example of an extensive pre-entry program is Sutton Trust, aimed at students from a non-privileged background (Sutton Trust, 2008). Sutton Trust prepared prospective students for higher education through summer courses and mentoring programs that mostly had a subject-focus and stimulated understanding of studying in university education. Follow-up interviews with participants indicated that most prospective students who attended this pre-entry program found the intervention helpful, because sample lectures and similar activities underlined their prior ideas and subject
preferences or, in contrast, confronted them with misconceptions (Sutton Trust, 2008; Thomas, 2011; Thomas, 2013). It was also helpful for prospective students to speak with current students and
academic staff since they could help them to (re)shape their ideas about studying in university (Sutton Trust, 2008).
A second study was conducted on the pre-entry intervention Preparation for Higher
Education. This program took place during an open event of the university (Murtagh, 2012). During
meetings, prospective students spoke with academic staff and current students about academic skills and academic assignments to get a clearer view on academic demands. Survey research among prospective students (N = 147) indicated that almost all students got a better understanding of
assignments used in university education after this meeting. It helped them to figure out the academic structure and nature of academic writing. The author suggests that these sessions could even be more successful when prospective students could practice with model exams or assessments, instead of only discussing those. A pre-entry program focused on practicing assessments could help students
understanding new assessment policies and internalizing a new learning context (Murtagh, 2012). Although large-scale research on general pre-entry programs is lacking, we can learn from the above-mentioned programs and their suggestions for future research.
Aside from research on pre-entry programs, known good practices in first-year university education can be helpful to unravel success factors of pre-entry programs. A frequently cited guideline comprising good practices for first-year university education is ‘Seven principles for good practice in undergraduate education’ developed by Chickering and Gamson (1987). They originally developed their seven principles aimed at improving undergraduate education by offering practical and widely applicable guidelines for faculty members, students and administrators. The seven principles are:
1. Encourage interaction between faculty and students: contact between students and faculty enhances students’ commitment to their studies.
2. Promote cooperation between students: collaboration between students increases
involvement, while sharing knowledge and ideas also stimulates cognitive processes and contributes to a better understanding of the subject.
3. Use active learning methods: students do not learn in a passive way, talking and writing about course content and relating and applying new concepts to prior knowledge promotes the learning process.
4. Provide prompt feedback: feedback on assignments and suggestions for improvement enhance students’ learning.
5. Focus on time on task: defining time related expectations helps students to make their learning process more effectively.
6. Set high expectations: high expectations of teachers and institutions work as a self-fulfilling prophecy and stimulate e.g. students’ preparation, motivation and performances.
7. Respect diversity: all students have talents and learning preferences, create the opportunity to meet their needs.
Since the introduction in 1989, the seven principles have been used frequently in
undergraduate institutes (e.g. Chickering & Gamson, 1999; Kuh, Kinzie, Schuh, & Whitt, 2010). More recent research has established the relationship between the principles and academic
performance, indicating for example that student collaboration is positively related to motivation and analytical skills (Carbera et al., 2002) and that a focus on time on task improves academic
performance (Johnstone, Ashbaugh, & Warfield, 2002). Moreover, a longitudinal cross-sectional study on the complete set of principles has proven that they are as a whole positively related to students’ learning attitude and their academic achievement (Cruce, Wolniak, Seifert, & Pascarella,
2006). In sum, the ‘Seven principles for good practice in undergraduate education’ offer a useful framework to assess good practices in pre-entry interventions.
Intervention: Matching Weeks
The Dutch Ministry of Education enforced the pre-entry study check for all prospective first-year students and universities in the Netherlands. However, universities decided on the format of the pre-entry study check. The current paper presents two studies that investigated the pre-entry study check called the Matching Week at one of the largest universities in the Netherlands. Before enrollment, all prospective students were required to attend a one-week program in their degree program of interest, existing of, e.g. lectures, seminars, self-study and assessments. The idea was that this Matching Week would be comparable to a regular study week in the first year of university education and therefore could provide prospective students the opportunity to align their expectations with their experiences during the week. After this week, university advised students non-binding whether they fit in the program or not, based on their performance on the assessment at the end of the
Matching Week. It was expected that experiences during the week and the advice confirmed
prospective students’ study choice or in contrast stimulate them to switch degree programs or universities.
One of the building blocks of Matching was shaping students’ expectations about program characteristics such as content, level of difficulty, learning environment and time investment. The
Matching Week aimed to help students to get insight in these aspects and to (re)consider their study
choice. Because of a scarcity of research on interventions comparable to Matching and their influence on prospective students’ view and decision-making, the two studies in this paper examine whether a
Matching Week contributed to students’ understanding of studying in university education. Since each
degree program organized its own Matching Week it was expected that not all degree programs would succeed in improving students’ understanding to similar extents. Hence, more insights in the program characteristics might therefore expose good practices and could provide input for further improvement of this pre-entry intervention.
Research questions
This paper examined to what extent the Matching Week contributed to students’
understanding of the degree program of their choice, whether it complemented other study choice activities and whether it had a role in students’ final study choice. Furthermore, the paper explored which program characteristics contributed to the success of a Matching Week, success being defined as giving better insights in several degree program aspects. This research aim resulted in two studies: Study 1 was an explorative quantitative study, while Study 2 used a qualitative approach and built on the results of Study 1. The following research questions are addressed in this paper:
Study 1
Research question 1: To what extent gives a Matching Week prospective students insights in the degree program characteristics level of difficulty, content, teaching methods, and expected time investment?
1a. Does a Matching Week give prospective students a better understanding of the degree program characteristics level of difficulty, content, teaching methods, and expected time investment?
1b. Which student background characteristics are related to the Matching Week’s contribution to a better understanding of a degree program?
1b. Which study behavior factors are related to the Matching Week’s contribution to a better understanding of a degree program?
Research question 2: To what extent complements a Matching Week other study choice activities? 2a. Does a Matching Week complement other study choice activities according to prospective students?
2b. Which student background characteristics are related to whether prospective students see a
Matching Week as a complementing study choice activity?
2c. Which study behavior characteristics are related to whether prospective students see a
Matching Week as a complementing study choice activity?
Research question 3: To what extent contributes a Matching Week to prospective students’ study choice?
3a. Does a Matching Week contribute to prospective students’ study choice?
3b. Which student background characteristics are related to whether a Matching Week contributes to prospective students’ study choice?
3c. Which study behavior characteristics are related to whether a Matching Week contributes to prospective students’ study choice?
Study 2
Research question 4: What are the program characteristics of Matching Weeks succeeding in improving prospective students’ understanding of a degree program?
Relevance
This research project aimed to contribute to the ongoing debate on how to improve retention rates in the first year of higher education. A large amount of research has been conducted on factors contributing to a successful transition into university education (e.g. Harvey et al., 2006; Tinto, 1994; Tinto, 2006; Zepke & Leach, 2005), but Matching was one of the first large-scale interventions that
transferred this knowledge about a successful transition into practice. Insights in the contribution of the Matching Week to prospective students’ understanding of a degree program and study choice add to evidence-based knowledge on manners to improve the transition fit. In addition, these results could also be helpful for universities, students and society. Insights can help students and universities to improve the student-degree program fit and might contribute to student retention and study success. A more successful transition from secondary into university education could decrease the amount of withdrawal in higher education. For society this means more graduates and a reduction of costs of higher education.
Study 1
Method Participants
3948 Prospective students of a large university in the Netherlands attended the Matching
Weeks of 44 degree programs in June 2014. 2554 Of them (65%) filled out at least one question on a
questionnaire about their experiences during the week. For reasons of reliability, only prospective students who filled out at least half of the questions per topic (i.e. study choice, study behavior, better understanding, effect on study choice) were included in this study, therefore the final data set
comprised 2272 students. Response rates varied across Matching Week programs from 18% up to 100% and were the highest for programs that used paper-pencil questionnaires.
Participants were asked to write down their student number on the questionnaire in order to link their response to institutional data on background characteristics. For 79% of the prospective students this connection could be made, while other participants did not provide their student numbers or filled out numbers that could not be matched with existing student numbers1. The procedure for missing data will be described in the data analyses section.
The majority of the participants were male (53%). The average age of the participants was 19.04 (SD = 3.13), varying from 16 years old to 72 years old. Most prospective students had a background in pre-university education (88%) and a Dutch nationality. Table 1 gives an overview of all background characteristics.
Procedure
The present study was conducted in cooperation with Matching Week coordinators of all degree programs. Before the Matching Week, questionnaires and instructions were distributed to coordinators and they were asked to administer these questionnaires to participants of the Matching
Week after the exam, but before students would receive a grade and study advice. The coordinators 1
There are several reasons for this mismatch. Students might have provided ‘false’ numbers on purpose so that they could not be tracked or it could be a fault in scanning.
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could decide on whether they used a paper-pencil questionnaire or online questionnaire. The questionnaire included a paragraph about the research aims and confidentiality of participation.
Prospective students were asked to fill out their student number in order to link their
responses to institutional data. These data were obtained from Studielink, the national admissions and enrollment office for higher education in the Netherlands that registers students’ prior education, diploma status and demographics. This data collection procedure is commonly used in institutional research and has been approved by the ethical committee of the participating university.
Table 1
Background Information Participants (N = 2272)
Background Variable Percentages
Gender
Male 53%
Female 47%
Prior education2
Pre-university education (vwo) 88%
Higher general secondary education (havo) 3% Higher vocational education (hbo) 5%
University education 1%
Colloquium doctum 1%
Other (e.g. non Dutch diplomas) or missing 1% Nationality
Dutch 98%
Europeans 1%
Other or missing 1%
Measures
Study 1 aimed to investigate to what extent a Matching Week contributes to a better
understanding of academic degree programs and study choice. A subset of questions of the Matching
Week evaluation form was used. This form covered several topics, varying from organization of the
week to students’ opinions about lectures, seminars and exams. The questions of interest for the current study asked about undertaken study choice activities, study behavior during the Matching
Week, and the effect of the week on students’ understanding of a degree program and their study
choice. All questions were tested in a pilot Matching Week in February and if necessary adapted for the edition in June. Details of the pilot Matching Week can be found in the management report (Strategie & Informatie, 2014).
First, prospective students were asked to fill out which study choice activities they had undertaken before they attended the Matching Week, for example reading information on a website or visiting an open day, because it was expected that the effect of the Matching Week might be affected
2
Prior education is based on students’ registration in Studielink. Officially, prospective students cannot enter university with a higher general secondary education diploma (havo). Probably, these students had to complete their pre-university
education (vwo) or where enrolled in a higher vocational education first year, which are both education levels admitted to university. Colloquium doctum refers to an entrance exam for people older than 21 who do not have required diplomas. Due to rounding, the percentages sum up to 99%.
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by previous undertaken study choice activities A distinction was made between study choice activities inside the university that organized the Matching Week and undertaken activities at other universities (Table 2). Students could choose more than one answer per question (inside versus outside the particular university) and their answers were per question summed up as an indicator for undertaken study choice activities.
Four questions asked about the number of hours spent on the Matching Week, difficulties with self-studying, satisfaction with study effort, and working hard (Table 3). The first question asked students to indicate the number of hours they spent on self-study during the Matching Week, while the other questions asked for agreement on three statements on a five-point Likert scale increasing from strongly disagree (1) to strongly agree (5).
Four other questions aimed to investigate whether the Matching Week helped students to get a better understanding of program aspects and whether the Matching Week affected students’ study choice. Prospective students were asked about the Matching Week’s contribution to a better understanding of a) the level of difficulty, b) content, c) teaching methods and d) expected time investment of the particular degree program (Table 4). Again, students reacted on a five-point Likert scale, increasing from strongly disagree (1) to strongly agree (5). Together these questions formed a scale measuring better understanding of the degree program. A reliability analysis indicated that these four items taken together were a reliable scale (α = .80).
Two questions focused on the effect of the Matching Week on students’ study choice. One question asked whether students experienced the Matching Week as complementary to other study choice activities, while the other question focused on whether prospective students’ participation in a
Matching Week had influenced their final study choice. Students reacted on a five-point Likert scale
increasing from strongly disagree (1) to strongly agree (5).
Background characteristics in this study were gender, age, level of prior education, and nationality, all retrieved from the Studielink database.
Data analyses
Data of all 44 Matching Week programs were merged to one file and matched with
institutional data on background characteristics. In addition, data were per degree program screened on plausible values and outliers with use of IBM SPSS 20. The variables number of undertaken study
choice activities inside this university, number of hours spent on self-study and age indicated several
outlier values, which were corrected to the largest or smallest non-outlier value plus or minus one within the particular degree program (Tabachnick & Fidell, 2012).
Data were missing on variables collected during the Matching Week and on variables obtained from Studielink. Missing data analysis with SPSS indicated that the data were not missing at random, therefore, data could not be imputed with the expectation maximization method. Since deletion of data would mean a loss of 21% of the data and a distortion of the sample, other data imputation
methods were examined (Tabachnick & Fidell, 2012). Considering that the data were grouped and it could be expected that the mean age, gender proportions and proportions for prior education were more similar within degree program than between degree programs, group means were inserted for missing values. Group means were also imputed for missing data on the questionnaire. After that, scale scores were calculated for the variable better understanding of the degree program and this variable was checked on outliers per degree program. Several outliers appeared and these were corrected to the largest or smallest non-outlier value plus or minus one within the particular degree program. Finally, descriptive statistics were calculated (Table 1).
In order to answer sub questions 1a, 2a, and 3a, descriptive statistics were calculated. For answering the other sub questions, multilevel analyses were conducted, because it was expected that responses of prospective students within one Matching Week program would be more similar to each other than responses of prospective students who followed a different Matching Week program. All variables were standardized, so that coefficients of interval variables could be interpreted as effect sizes Pearson’s r and the coefficients of the dummy variables as effect size Cohen’s d (Cohen, 1992).
Sub questions 1b and 1c intended to investigate which factors contributed to a better understanding of a degree program. In the analysis the scale score for better understanding of a
degree program was the dependent variable, while age, gender, prior education, undertaken study choice activities, number of hours spent on self-studying, difficulties with self-studying, satisfaction with efforts, and working hard were independent variables. Based on a random-intercept-only model
(Model 0), the intraclass correlations were calculated examining the dependency of the data.
Background characteristics were included in the first model, while in the second model study choice activities were added. The final model, Model 3, also contained variables regarding study behavior during the Matching Week. Model fits and improvements of these models were evaluated through the -2 log likelihood method. A chi-square test was conducted to explore the dependency between better understanding of a degree program and the different Matching Week programs.
Sub question 2b and 2c aimed to explore which factors were related to whether Matching
Weeks complement other study choice activities. This series of analyses took Matching Weeks as complementary activity as dependent variable and age, gender, prior education, undertaken study choice activities, number of hours spent on self-studying, difficulties with self-studying, satisfaction with efforts, working hard, and better understanding of a degree program as independent variables.
Again a step-wise procedure was used, starting with an intercept-only model in order to examine the dependency of the data, followed by two models with background characteristic and finally two models containing study behavior related variables.
The final series of analyses focused on sub questions 3b and 3c asking whether Matching
Weeks’ contribution to study choice was related to age, gender, prior education, undertaken study choice activities, number of hours spent on self-studying, difficulties with self-studying, satisfaction with efforts, working hard, better understanding of a degree program, and the complementing
character of the week. Again, intraclass correlations were calculated to check dependency of the data
(Model 0). Background characteristics were added in Model 1 and 2 and other groups of independent variables were included in Model 3, 4 and 5. The model fit of these models was evaluated through the -2 log likelihood method.
Results Preliminary analysis
Descriptive statistics of background characteristics are presented in Table 1. Table 2 contains information on the study choice activities prospective students undertook before the Matching Week. The results indicated that most prospective students visited websites of degree programs to gain information, followed by visiting one or more information sessions of degree programs of interest. It was striking that 7.7% (N = 176) of the respondents was not engaged in any other study choice activity than the Matching Week.
Table 2
Study Choice Activities Undertaken Inside and Outside this University (N = 2272)
Study choice activity Inside this university (%) Outside this university (%)
Study interest test 5.9 35.9
Study coach consultation 6.5 16.5
Visited websites of degree programs 64.9 79.8
Visited an information session of one degree program
32.3 23.0
Visited information sessions of more than one degree program
42.0 56.9
Visited information session at secondary school
34.1
No study choice activities undertaken 7.7 7.3
Other type of study choice activity 9.2 7.6
Prospective students spent in general less than five hours on self-studying during the
Matching Week. Results for difficulties with self-studying, satisfaction with study efforts, and working hard showed that students tended to choose the central score on a five-point Likert scale. Thus, they
had no difficulties with self-studying and were neither satisfied nor unsatisfied with their effort during the week and neither agreed nor disagreed on working hard during the Matching Week (see Table 3).
Table 3
Means and Standard Deviations Study Behavior Variables (N = 2272)
Study behavior
The number of hours I spent on self-studying is % 0-5 hours 60.7 6-10 hours 26.4 11-15 hours 8.5 16-20 hours 2.7 21-25 hours 1.4 >25 hours .3 M SD
I had difficulties with self-studying. 2.57 .97
I am satisfied about my efforts during this trajectory 3.11 1.02
I worked hard during this trajectory 2.82 1.01
Factors related to better understanding of degree program
Research question 1 asked to what extent a Matching Week gave prospective students a better understanding of a degree program. Prospective students agreed that they had a somewhat better understanding of level of difficulty (M = 3.75, SD = .92), content (M = 3.76, SD = .91), teaching methods (M = 3.61, SD = .93) and expected time investment (M = 3.52, SD = .94) (Table 4).
Table 4
Means and Standard Deviations Better Understanding and Study Choice Variables (N = 2272)
Variables M SD
Scale better understanding of degree program (α = .80) As a result of Matching I have a better understanding of
the educational level of the degree program than before 3.75 .92 the subject-content of the degree program than before 3.76 .91 the teaching methods of the degree program (lectures, self-study) than before 3.61 .93 As a result of Matching I know better how much effort I should put in studying 3.52 .94 Effects on study choice
To me, Matching complements other study choice activities well
(inside and outside this university) 3.46 1.08
Matching has contributed to my study choice
(whether I register for this degree program or not) 2.94 1.28
Via multilevel analyses was examined how background characteristics and study behavior during the week were related to a better understanding of a degree program (sub questions 1b and 1c). In order to verify the hypothesized dependency of data, the intraclass correlations (estimated ρ or ICC) were calculated (Table 6). An ICC above 0 indicates that the data are dependent, while an ICC larger than .20 indicates strong dependency. The ICC score for this model was .063, meaning that the data were dependent on each other. Table 7 presents the multilevel models with predictors for better
understanding of the degree program. The final model, Model 3, fitted the data best. Additional
analyses indicated that none of the predictors had a random slope, meaning that the slopes across groups do not significantly differ from each other.
Table 5
Intraclass Correlation for Multilevel Models
Better understanding Complements other activities Influence study choice σ2 e = Var(Eij) .958 .971 .969 τ00 = Var(U0j) .064 .038 .047 ICC (ρ) .063 .038 .046
The results of Model 3 indicated that age, gender, prior education, and prior undertaken
study choice activities were not related to students’ understanding of a degree program. Concerning
study behavior, the number of hours spent on studying (B = .059, p = .02), difficulties with
self-studying (B = .066, p < .01) and students’ opinion about whether they worked hard (B = .139, p < .01)
were significant positive predictors of a better understanding of the degree program. Thus, the more hours prospective students spent on self-studying, the better their understanding of the degree program. In addition, students who experienced difficulties with self-studying and those who worked harder during the trajectory had a better understanding of a degree program after the week. The found effects for study behavior variables were small. Explained variances were calculated for the final model, Model 3, and indicated that this model explained 5% of the variance at the student-level and 26% of the variance at the degree program level.
Although students in general indicated they had a better understanding of the degree program, it was expected that this would also depend on the specific week they attended. A chi-square test confirmed this hypothesis3 indicating that there was a significant association between a better
understanding of the level of difficulty ((172, N = 2212) = 274.503, p < .01), content ((172, N = 2220) = 244.785, p < .01), teaching methods ((172, N = 2199) = 297.850, p < .01), and expected time investment ((172, N = 2224) = 357.309, p < .01) and the particular Matching Week program (Table 5). This demonstrated that the programs were not equally successful in giving students a better
understanding of studying in university education. A post-hoc test indicated how many standard deviations the scores for a particular program deviated from the expected association. A score of plus or minus two or higher indicated a large deviation and a score of plus or minus three even a very large deviation (Agresti & Finlay, 2008). Several degree programs highly differed from the expected score and were further explored in Study 2.
3
The chi-square test was performed on the original data of included participants, thus without the imputation of group means, because the imputation of group means makes that this variable no longer consists of separate categories. However, it is assumed that the distribution of the imputed data is in line with the original non-missing data.
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Table 6
Chi Square Test for Association Between Matching Week and Better Understanding Program Characteristics
Matching Week program * χ2 df p
Better understanding level of difficulty 274.503 172 <.01
Better understanding content 244.785 172 <.01
Better understanding teaching methods 297.850 172 <.01
Better understanding expected time investment 357.309 172 <.01
Factors related to Matching Week as complementing study choice activity
Research question 2 asked to what extent Matching Weeks complemented other study choice activities. Descriptive statistics indicated that prospective students found Matching complementary to other study choice activities (M = 3.46, SD = 1.08) (Table 4). In addition, multilevel analyses were conducted to investigate which variables predicted whether students experienced a Matching Week as complementary to other study choice activities (Table 8). After the intercepts only model (Model 0), a stepwise model build procedure was followed. Based on a -2 log likelihood evaluation, Model 4 seemed to be a better model than previous ones. Additional analyses indicated that none of the predictors had a random slope, meaning that the slopes across groups did not significantly differ from each other.
Table 7
Fixed Effects Estimates (Top) and Variance-Covariance Estimates (Bottom) for Models of the Predictors of Better Understanding Study Program
Parameter Model 0 Model 1 Model 2 Model 3
Intercept -.017 (.049) -.023 (.049) -.020 (.048) -.024 (.043)
Fixed Effects Level 1 (student specific)
Student Characteristics
Age -.000 (.024) .006 (.024) -.010 (.024)
Gender -.005 (.023) -.007 (.023) -.021 (.022)
Prior Education
Pre-university education -.058 (.064) -.066 (.064) -.053 (.063)
Higher general secondary education -.016 (.039) -.016 (.039) -.002 (.039)
Higher vocational education .035 (.049) .034 (.049) .034 (.048)
University education -.011 (.029) -.013 (.029) -.008 (.029)
Colloquium doctum .000 (.029) .001 (.029) .009 (.029)
Study choice
Study choice activities inside this university -.024 (.022) -.030 (.022) Study choice activities outside this university .052* (.023) .040 (.022)
Study behavior
Hours spent on self-studying .059* (.026)
Self-studying difficult .066** (.022)
Content about efforts -.021 (.028)
Worked hard during Matching Week .139** (.029)
Random Parameters
Level 1 – variance .958** (.029) .952** (.029) .951** (.029) .923** (.027) Level 2 – variance .064** (.024) .060** (.022) .058** (.022) .043* (.019)
-2*log likelihood 6400.922 6386.291 6380.967 6307.345
Note: Standard errors are in parentheses. Dummy variables: gender: 0 = male, 1 = female; prior education: 0 = no, 1= yes.
* p < .05; ** p < .01.
The background characteristics age, gender and prior education did not affect students’ opinion about the complementary character of the Matching Week. However, the number and type of
undertaken study choice activities inside the university were a significant positive predictor of the
complementing character of the Matching Week (B = .039, p = .032), indicating that students that undertook several types of study choice activities within the particular university found the Matching
Week more complementing to other study activities than students that were less involved in study
choice activities at the particular university. However, the found effect was small.
Furthermore, working hard was a significant positive predictor (B = .039, p = .03), meaning that students who found that they worked hard during the week tended to see the Matching Week as a more complementing study choice activity, but the effect was small. Moreover, it seemed that the
Matching Week was of value for students who got a better understanding of the degree program due
to their participation in the week (B = .564, p < .01). The size of this effect was moderate. Model 4 explained 37% of the variance at the student level, while 71% of the variance between degree programs was explained.
Factors related to the influence of a Matching Week on study choice
Lastly research question 3 examined to what extent a Matching Week contributed to prospective students’ study choice. Students tended to say that the week did not affect their study choice (M = 2.94, SD = 1.28) (Table 4). However, the standard deviation of this last question was 1.28, indicating that there was quite some variation across students, thus some found the Matching
Week helpful for their study choice, while others did not find it helpful.
Via a multilevel analyses was investigated for which participants’ study choice was influenced by their experiences gained during the Matching Week (Table 9). Starting with an intercepts-only model a more elaborated model containing independent variables was built and compared to previous models using the -2 log likelihood evaluation method. Model evaluation showed that Model 5 fitted the data best. Additional analyses demonstrated that none of the predictors had a random slope, meaning that the slopes across groups did not significantly differ from each other.
Based on Model 5, it could be concluded that Matching Week’s influence on study choice was not affected by age, prior education and undertaken study choice activities. In contrast, gender was a significant predictor. Female students’ study choice was affected significantly more than male students’ study choice (B = .040, p = .04). However, the effect size was small.
Results further indicated that students who got a better understanding of a degree program during the Matching Week let affect their study choice significantly more than students who did not get a better understanding of the degree program (B = .184, p < .01). The size of this effect was small. Also the complementing character of the Matching Week influenced prospective students’ study
choice. Prospective students who found the week complementary to other study choice activities, valued the Matching Week more in their study choice (B = .376, p < .01). The effect size indicated that the effect was small. The explained variances at the student and degree program level were calculated and showed that Model 5 explained 29% of the variance between students and 66% of the variance between degree programs.
In sum, research questions 1, 2, and 3 aimed to investigate whether a Matching Week could give prospective students a better understanding of a degree program, whether it complemented other study choice activities and whether it contributed to prospective students’ study choice process. According to students, the Matching Week helped them to get a better understanding of a degree program. Moreover, they found the Matching Week complementary to other study choice activities, but neither agreed nor disagreed that the Matching Week had influenced their study choice. The number of hours spent on the Matching Week, the experienced difficulty with self-studying and the perceived efforts students put in the week contributed significantly positive to students’ understanding of the degree program. Prospective students who worked hard during the week and those who got a better understanding of the degree program saw Matching as a complementing activity. Also the number of undertaken study choice activities within the university that organized this Matching Week predicted the complementing character of it. The Matching Week affected especially female students’ study choice. Finally, the Matching Week affected the study choice of students who got a better understanding of a degree program and who experienced the week as a complementary to other study choice activity.
Table 8
Fixed Effects Estimates (Top) and Variance-Covariance Estimates (Bottom) for Models of the Predictors of the extent to which Matching Week Complements Study Choice Activities
Parameter Model 0 Model 1 Model 2 Model 3 Model 4
Intercept .035 (.042) .030 (.040) .030 (.039) .009 (.037) .011 (.022)
Fixed Effects Level 1 (student specific)
Student Characteristics
Age .005 (.024) .011 (.024) -.007 (.024) -.001 (.019)
Gender .002 (.023) .000 (.023) -.013 (.022) -.001 (.018)
Prior Education
Pre-university education -.069 (.064) -.075 (.064) -.061 (.063) -.035 (.052)
Higher general secondary education -.011 (.039) -.011 (.040) .005 (.039) .005 (.032)
Higher vocational education .016 (.049) .016 (.049) .017 (.048) -.003 (.039)
University education -.032 (.029) -.031 (.029) -.028 (.029) -.026 (.023)
Colloquium doctum -.042 (.030) -.042 (.030) -.032 (.029) -.038 (.024)
Study choice
Study choice activities inside this university .031 (.022) .021 (.022) .039* (.018)
Study choice activities outside this university .044 (.023) .033 (.022) .009 (.018)
Study behavior
Hours spent on self-studying .035 (.025) -.008 (.020)
Self-studying difficult .020 (.022) -.022 (.018)
Content about efforts .015 (.028) .035 (.023)
Worked hard during Matching Week .193** (.029) .116** (.024)
Effect Matching Week
Better understanding of degree program .564** (.017)
Random Parameters
Level 1 – variance .971** (.029) .966** (.029) .963** (.029) .919** (.028) .628** (.019) Level 2 – variance .038* (.017) .033* (.016) .031* (.015) .025 (.014) .004 (.005)
-2*log likelihood 6417.014 6403.721 6395.213 6285.731 5403.524
Note: Standard errors are in parentheses. Dummy variables: gender: 0 = male, 1 = female; prior education: 0 = no, 1= yes.
* p < .05; ** p < .01.
Table 9
Fixed Effects Estimates (Top) and Variance-Covariance Estimates (Bottom) for Models of the Predictors of Matching Week’s Influence on Study Choice
Parameter Model 0 Model 1 Model 2 Model 3 Model 4 Model 5
Intercept .013 (.045) -.004 (.043) -.003 (.042) -.016 (.040) -.009 (.032) -.013 (.026)
Fixed Effects Level 1 (student specific)
Student Characteristics
Age .034 (.024) .042 (.024) .031 (.024) .034 (.022) .034 (.021)
Gender .039 (.023) .036 (.023) .029 (.022) .038 (.020) .040* (.019)
Prior Education
Pre-university education .001 (.064) -.007 (.064) .006 (.063) .026 (.058) .037 (.055)
Higher general secondary education .042 (.039) .043 (.039) .055 (.039) .056 (.036) .054 (.034)
Higher vocational education .075 (.049) .076 (.049) .078 (.048) .065 (.044) .065 (.042)
University education -.032 (.029) -.032 (.029) -.029 (.029) -.026 (.027) -.017 (.025)
Colloquium doctum -.028 (.02 -.028 (.029) -.021 (.029) -.025 (.027) -.011 (.025)
Study choice
Study choice activities inside this university .187 (.022) .014 (.022) .026 (.020) .013 (.019) Study choice activities outside this university .061** (.023) .053* (.022) .037 (.021) .034 (.020)
Study behavior
Hours spent on self-studying .022 (.025) -.003 (.023) -.003 (.021)
Self-studying difficult .044* (.022) .016 (.020) .022 (.019)
Content about efforts .026 (.028) .036 (.026) .026 (.024)
Worked hard during Matching Week .102** (.029) .047 (.027) .005 (.026)
Effect Matching Week
Better understanding of degree program .395** (.019) .184** (.022)
Complements other study activities .376** (.022)
Random Parameters
Level 1 – variance .969** (.029) .958** (.029) .954** (.029) .937** (.028) .796** (.024) .711** (.021) Level 2 – variance .047* (.019) .042* (.018) .039* (.018) .033* (.016) .017 (.010) .008 (.007)
-2*log likelihood 6417.400 6390.036 6379.208 6335.374 5956.147 5690.541
Note: Standard errors are in parentheses. Dummy variables: gender: 0 = male, 1 = female; prior education: 0 = no, 1= yes.
* p < .05; ** p < .01.
Study 2
The second part of this paper covers the fourth research question focusing on program characteristics contributing to the success of a Matching Week. Success was defined as follows: 1) the week gave students better insights in a degree program, 2) complemented previous undertaken study activities and 3) had an effect on their study choice. Study 2 was an explorative and qualitative study consisting of in-depth semi-structured interviews with teachers, coordinators and project leaders of the
Matching Week. The goal was to get an understanding of their considerations during the development
of the Matching Week.
Method Participants
The selection of participants for this study was based on the previous reported quantitative analyses. Study 1 suggested that a Matching Week especially contributed to study choice when the program added understanding in the particular degree program. A chi-square test and post-hoc analysis indicated which programs were succesfull and less successful in improving students understanding of degree program characteristics. In addition, multilevel analyses showed that the amount of time spent on self-studying and effort put in the Maching Week were significant predictors for the succes of the Matching Week.
Four degree programs of different faculties (Faculty of Economics & Business, Faculty of Humanities, Faculty of Science and Faculty of Social Sciences) were selected for further exploration, two scoring relatively high on better understanding of the degree program and efforts and two scoring relatively low on these aspects. Seven interviews were held with teachers and coordinators of the four programs (Table 11). Besides that, two project leaders of the Matching Weeks were interviewed because of their overarching insights in the 44 Matching Weeks.
Procedure
Stakeholders of interest were informed about the study and asked if they were willing to be interviewed about their experiences with the development and their evaluation of the Matching Week. Semi-structured interviews were held at the workplace of the stakeholder, one interview was held via telephone. The interviews lasted on average 45 minutes. Interviews were only with explicit consent recorded and afterwards transcribed. A report of the interview was sent to each interviewee for a consent.
Table 11
Characteristics of Interviewees
Participant Degree program Daily job Role Matching Week
1 Program A,
± 1000 first-year students
Coordinator exchange and intake
Coordinator
2 Program A,
± 1000 first-year students
Assistant professor Lecturer
3 Program B,
± 300 first-year students
Assistant professor, coordinator year 1
Coordinator, examiner
4 Program C,
± 100 first-year students
Project leader several projects Coordinator
5 Program C,
± 100 first-year students
Bachelor programs’ director Program director
6 Program D,
± 100 first-year students
Assistant professor, coordinator year 1
Lecturer, developer
7 Program D,
± 100 first-year students
Teacher seminars Teacher seminars, developer
8 Executive staff Senior advisor Project leader
9 Executive staff Full professor, director
education institute
Project leader
Measures
The semi-structured interviews focused on key characteristics of the Matching Week program namely curriculum design, content, study load, level of difficulty, teaching methods, self-study, study behavior, assessments and evaluation. An interview guideline was developed covering all these aspects (see Appendix). A question about curriculum design was for example “Which were the
starting points for designing the Matching Week for your degree program?” while a question about
study load was “To what extent was the study load comparable to a regular study week?”. Interviewees were in addition invited to reflect on their choices. Reflection questions about the
Matching Week were for instance “Which elements are according to you success factors of the week?” or “What would you advice other degree programs regarding the Matching Week?”
Data analysis
All interviews were transcribed in order to conduct a qualitative content analysis (Silverman, 2006). A qualitative content analysis is a thematic analysis in which quotations are used to illustrate sets of categories (Wilkinson, 2004 as cited in Silverman, 2006). Interview data were coded according to pre-defined categories, namely key program characteristics, and allocated to a data scheme. Besides that, new categories appeared from the data, for instance social and academic integration and the meaning of the study advice, and these categories were added to the data scheme. The data scheme was used to identify patterns that could explain why some Matching Weeks tended to be more successful in providing students better insights in a degree program than others.
Results
Below, the main characteristics explaining success of the Matching Weeks that emerged during the analysis are described.
Content of the Matching Week
Regarding the content selection for the Matching Week, all interviewees mentioned they thought about core subjects in their discipline and courses frequently experienced as obstacles. Making use of these considerations, coordinators and teachers chose particular subjects. While some degree programs used preexisting courses as a starting point, those degree programs successful in providing students a better understanding of the degree program tended to start from scratch and developed specific components for the Matching Week. The successful degree programs built their
Matching Week around one specific topic or book that students were able to study within one week. In
addition, focusing on a particular topic made it possible to avoid problems stemming from a lack of prior knowledge.
“It should be something that could be taught in one week. That was an important argument.”
(P1, lecturer)
“It must be something understandable and applicable. So this was a concept that could be taught in a short time and that could be used in the observation assignment. Students should think: ‘I can use this theory immediately and it shows me something that I wouldn’t have seen without this knowledge’.”
(P7, seminar teacher)
Furthermore, in comparison to the less successful programs, more successful Matching Weeks incorporated academic research methodology in their curriculum. Students had for example a lecture on observation techniques and practiced this methodology as a part of an assignment or worked through an online lecture about content analysis and applied knowledge gained to a case. Incorporation and application of research methods might have helped students to get a better
understanding of the academic component of the degree program. One of the interviewees mentioned that it was sometimes difficult for prospective students to understand research methodology within his discipline:
“They [prospective students] find it somewhat difficult to understand this type of research. They find it difficult to understand the methodology used in our discipline, they tend to think that you should administer questionnaires [instead of doing for example a content analysis].”
(P3, coordinator)
A content related consideration that might have contributed to the success of particular
Matching Weeks concerned the desire to position the discipline in a broader context. One degree
program included for example a lecture about a theory that emphasized the urgency of the discipline. During this lecture students were not only taught about the specific theory, but the theory itself also served as inspiration and motivation for students’ study choice.
“It [the chosen theory] is a very radical view, but it has a function in underlining the
importance of our discipline. That is the function of the lecture. It marks why you should study our discipline from our disciplines’ point of view. […] It motivates students. If you have to explain why you chose to study this discipline, you can use the in the lecture given
arguments.”
(P3, coordinator)
Another successful degree program used the Matching Week to emphasize the differences between the research methodology used in their department and methodology used in departments elsewhere in the country. Hence, the Matching Week provided an opportunity to position their degree program within the national context.
Two degree programs are also taught as a subject in secondary education. During the
interviews stakeholders emphasized the importance of explaining prospective students the difference between their subject at secondary level and at tertiary level. The types of questions addressed in courses in university differ from those in secondary education and are easier than topics covered in the bachelors’ degree. The teachers found it important to emphasize these differences, because it often leads to misconceptions about the content. One teacher heard from prospective students that they saw a difference between their course in secondary education and the Matching Week:
“They found it [content] original. Most of them have had such a course in pre-university education, but there they do more general things… Well, it’s somewhat easier what they do there than what we do here at university.”
(P5, program director)
Study load
All coordinators and teachers noticed that study load of the Matching Week was comparable to a regular study week in the first year of university education. Degree programs chose for instance materials, assignments and exams that were previously used in first-year education or developed content based on experiences or guidelines (e.g. number of pages per week) in the regular first year. The more successful Matching Weeks included components stimulating students to really experience the study load of a regular week in order to reduce the expected lack of commitment stemming from the non-binding study advice.
“Going to a lecture and reading a text, that’s not what studying is about. […] But we said: ‘Education means that you go to your seminar in which you should discuss and talk about the articles you’ve read. That’s what university education is about. You’ve to acquire knowledge, learn for your exam, write a piece, but it is also going to seminars and reflection on what you’ve read’.”
(P3, coordinator)
More successful degree programs included for example assignments that student had to hand in before class and would be graded, or scheduled time in which students worked together on
assignments. These degree programs tried to activate students to make them aware of the expectations regarding time investment and effort in the regular degree program.
Moreover, degree programs tended to differ in their expectations of the effort students wanted to put in the Matching Week. The programs less successful in giving students a better understanding of the degree program did not expect students being that enthusiastic about the Matching Week. During the interviews respondents emphasized that the Matching Week was scheduled directly after the national secondary school exam weeks, a period in which most youths spend their holidays abroad. Therefore, some interviewees did not expect that prospective students were willing to invest the requested time in the Matching Week. On the other hand, those programs succeeding in providing students a better understanding of the degree program underlined that the Matching Weak must require serious time and effort, because in general students quit their studies for the reason of combining it with too many other activities such as jobs. Thus, Matching Weeks should according to them have a substantial workload.
“You must feel the severity of a Matching Week, even if you don’t actively participate, even then you must see it in the obtained grade. […] It must be possible to fail the exam.”
(P3, coordinator)
Self-study
Concerning study behavior and self-study, prospective students tended to behave differently across the Matching Weeks. While some teachers and coordinators spoke about interested and hardworking prospective students, others spoke about a calculative way of learning. All degree programs provided study materials beforehand, but interviewees experienced that students started (if they even started) with studying after the lectures.
“I expected that [students wouldn’t spend time] beforehand. It had no consequences to them. So if there are no consequences, why should you work on in? I can understand them.”
(P2, lecturer)
Furthermore, one degree program experienced prospective students were focusing on the study advice and whether this would be binding or not.
“It was very difficult to motivate student for self-study. They asked a lot of questions about the role of the exam and the study advice. They wanted to know whether the study advice would be binding or not and their efforts depended on that.”
(P1, coordinator)
Other degree programs experienced interested students who did their best and handed in clearly written assignments. In particular the more successful degree programs focused on manners to avoid anonymity in class so that prospective students could not hide behind the efforts of others. One interviewee speaks for instance about tricks to encourage students to put effort in self-study.
“And a week means a real week filled with self-study. We developed our program in such a way that students did that [self-studying]. We implemented some tricks. For example, at the exam day students had to come to a seminar, so the examination was more than only a multiply choice test of which they knew that they didn’t have to pass it. […] Part of the examinations was a seminar during which students prepared a debate in which everyone should participate. And they received a grade for that part: pass or fail.”
P3, (coordinator)
This particular degree program made self-study also essential for one part of the final exam by offering content and exercises via an online lecture. One of the fragments covered in the online lecture would be questioned in the exam and it thus became necessary for students to work through the whole online lecture in their own time.
Another trick used by degree programs to encourage self-study was making students’ efforts visible. These degree programs created a certain social pressure by asking students to prepare a debate that would be held in class or by asking students to collaborate in an assignment. Moreover, two teachers mentioned the fact that their students had to hand in an assignment before class that would be discussed in class. According to these teachers none of the prospective students wanted to lose faces in front of their new teachers and one teacher had the impression that students asked family or friends feedback before they handed in their assignment.
“At Tuesday they had to work on an assignment at home. That’s somewhat scary, the first thing you have to hand in for university. I think all of them asked someone at home to read their assignment before handing it in. It [Dutch language] was faultless.”
(P7, seminar teacher)
Another program tried to support students’ self-study by offering them the opportunity to contact e-coaches (student-assistants) who answered questions about lectures, readings, and assignments. Unfortunately this did not work out in the way the degree program hoped for: Hardly any student took advantage of this opportunity.
Levels of difficulty
All Matching Week coordinators and teachers chose for materials used in the regular first-year degree program or materials comparable to those in the regular first-year program. Regarding level of difficulty, interviewees did not differentiate prospective students from regular first-year students. They all emphasized the importance of offering students a representative week. However, some academic texts seemed too difficult to study independently. Also during classes teachers treated prospective students as regular students: One seminar teacher explained that she asked students questions about the lecture to show them that studying comes not without obligations.
“I thought about the first seminar in the first week. I imagined: ‘You are first-year students, you just finished secondary education, and that’s how we start’. I wanted to make them aware of it. ‘Now you’re students, so I expect something of you. At least, that you’ve thought about the literature’.”
(P7, seminar teacher)
Nevertheless, all teachers and coordinators realized that the Matching Week was for most prospective students their first experience with studying in university education. Interviewees took this into account by offering students extra guidance, by providing them information on how to study
or the deployment of student-assistants who helped prospective students to find their way in the university building and to answer questions.
“Our study counselors put tips on Blackboard [online learning platform] about what’s a lecture about, how do you make notes during the lecture, how could you prepare yourself for the exam. We tried to offer them guidance on preparation.”
(P1, coordinator)
Representativeness of teaching methods
All interviewees said they used teaching methods that are also used in the regular first year in order to provide students with a representative image of university education.
“We wanted to give them a truthful picture of a regular study week, as far as possible.”
(P1, coordinator)
Though not all teaching methods of the first year were programmed during the Matching
Week. One degree program scheduled only lectures, because the size of their student population (over
thousand prospective students) made it too expensive to schedule seminars.
“It would be good to schedule two hours of lectures and two hours of seminars, but that means they [students] have to invest twice as much time, and we four times as many teachers. So how much do you want to invest in it? “
(P2, lecturer)
A similar comment was made by a program director of a science degree program. Normally, first-year students in his degree program have daily scheduled “on campus” education, for example lectures, seminars, but also practical or lab training. The Matching Week format seemed to be difficult to apply in the Faculty of Science, considering that daily education including lab training would offer the most representative picture of studying in science degree programs. However, this would be very expensive and time consuming for teachers.
Successful Matching Week programs scheduled several types of teaching methods such as lectures, seminars, self-study and group work. In addition, one program departed from the traditional lecture, which mostly lasts two hours and in contrast scheduled several one hour lectures. Moreover, it seemed to be important to opt for small-scale teaching methods that make students’ efforts visible for teachers and avoid students from hiding behind the effort of fellow students.
