Journal of Education for Business ISSN: 0883-2323 (Print) 1940-3356 (Online) Journal homepage: http://www.tandfonline.com/loi/vjeb20 Academic Performance of College Students: Influence of Time Spent Studying and Working Sarath A. Nonis & Gail I. Hudson To cite this article: Sarath A. Nonis & Gail I. Hudson (2006) Academic Performance of College Students: Influence of Time Spent Studying and Working, Journal of Education for Business, 81:3, 151-159, DOI: 10.3200/JOEB.81.3.151-159 To link to this article: https://doi.org/10.3200/JOEB.81.3.151-159 Published online: 07 Aug 2010. Submit your article to this journal Article views: 6550 Citing articles: 57 View citing articles Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=vjeb20 Academic Performance of College Students: Influence of Time Spent Studying and Working SARATH A. NONIS GAIL I. HUDSON ARKANSAS STATE UNIVERSITY JONESBORO, ARKANSAS ABSTRACT. Today’s college students are less prepared for college-level work than their predecessors. Once they get to college, they tend to spend fewer hours studying while spending more hours working, some even full time (D. T. Smart, C. A. Kelley, & J. S. Conant, 1999). In this study, the authors examined the effect of both time spent studying and time spent working on academic performance. The authors further evaluated the interaction of motivation and ability with study time and its effect on academic performance. The results suggested that nonability variables like motivation and study time significantly interact with ability to influence academic performance. Contrary to popular belief, the amount of time spent studying or at work had no direct influence on academic performance. The authors also addressed implications and direction for future research. Copyright © 2006 Heldref Publications T oday’s college students are spending less time studying. The fall 2003 survey conducted by the Higher Education Research Institute at UCLA’s Graduate School of Education and Information Studies found that only 34% of today’s entering freshmen have spent six or more hours per week outside of class on academic-related work (e.g., doing homework, studying) during their senior year in high school. The sample consisted of 276,449 students at 413 of the nation’s 4-year colleges and universities (over one fourth of entering freshmen in the United States), and the data were statistically adjusted to reflect responses of all firsttime, full-time students entering all four-year colleges and universities as freshmen in 2003. In fact, in 1987 when this question was asked of entering freshmen, 47.0% claimed they spent 6 or more hours per week studying outside of class. Since then, the time spent studying outside of class has declined steadily each year (Higher Education Research Institute, 2003). Another trend that is emerging is the increase in the number of college students who are employed either part time or full time. According to Gose (1998), 39% of college freshmen work 16 or more hours per week, an increase of 4% since 1993. Among all business majors, marketing students typically work even more hours per week than do other stu- dents (Smart, Tomkovick, Jones, & Menon, 1999). The 2002 survey conducted by the Higher Education Research Institute also found that 65.3% of entering freshmen have either “some concern” or “major concerns” about not having enough money to complete their college degrees (Higher Education Research Institute, 2002). This was an increase of almost 1% from 2001 and is likely to increase in the years ahead because of reduced funding for higher education by state legislatures. Although more women (70.9%) were concerned about whether they would have enough funds to complete college than were men (58.3%), all students seemed to be working out of the need to make up for rising tuition and fewer available grants. In summary, the proportion of college students who are employed either part or full time is likely to increase in the years to come, leaving greater numbers of students with less time for academic work. Students spending less time studying and more time working are two trends that all colleges and universities will have to confront. Lowering academic standards by rewarding minimum effort and achievement (expecting less) is certainly a short-term strategy, but one that will have negative long-term consequences. A more productive way to handle these concerns is to conduct empirical research to determine to what extent these trends will January/February 2006 151 negatively impact the academic performance of college students and use the findings from these studies to improve our academic programs. The influence that personal variables, such as motivation and ability, have on academic success is well documented, but there is a paucity of research investigating the influence that time college students spend on various activities such as studying outside of class and working has on their academic success. One reason for a lack of research in this area may be the common belief among most students and academicians that more time spent studying outside of class positively influences academic performance and that more time spent working negatively influences academic performance. Another, more plausible reason for this lack of research may be the complex nature of these relationships when evaluated in the presence of other variables, such as student ability and motivation. For example, it is likely that time spent studying outside of class will have a differential impact on the academic performance of college students who vary in ability. That is, the relationship that ability has with student performance will be stronger for those students who spend more time outside of class studying than for students who spend less time studying. With this study, we attempted to fill this void in the literature. First, we attempted to determine the direct relationship that time spent on academics outside of class and working had on academic performance among business students. Second, we attempted to determine whether the time spent on academics outside of class interacts with variables, such as student ability and motivation, in influencing the academic performance of business students. Hypotheses Tested It is commonly believed that students who spend more time on academicrelated activities outside of class (e.g., reading the text, completing assignments, studying, and preparing reports) are better performers than students who spend less time on these activities. There is some empirical support for this belief. For example, Pascarella and Terenzini 152 Journal of Education for Business (1991) found that the study habits of freshmen relate significantly to their first year cumulative grade point average (GPA). In their investigation of 143 college students, McFadden and Dart (1992) reported that total study time influenced expected course grades. In contrast, Mouw and Khanna (1993) did not find study habits to significantly improve the explanatory power of the first year cumulative GPA of college students. Ackerman and Gross (2003) have found more recently that students with less free time have a significantly higher GPA than those with more free time. Because of this conflicting evidence, there is a need to reinvestigate this relationship. Thus, our first hypothesis was H1: There is a relationship between time spent studying outside of class and academic performance. Along with the present trend of students spending less time on academicrelated activities, a growing number of college and university administrators are concerned that today’s postsecondary students are working more hours than their counterparts were years ago (Gose, 1998). It can be reasonably assumed that working more hours per week will leave students less time for studying outside of class and that this will negatively influence their academic performance. Although working more hours per week can be one key reason for a student to be in academic trouble, available research does not seem to support this hypothesis. Strauss and Volkwein (2002) reported that working more hours per week positively related to a student’s GPA. Light (2001), who interviewed undergraduate students of all majors, found no significant relationship between paid work and grades. According to Light, “students who work a lot, a little, or not at all share a similar pattern of grades” (p. 29). Because empirical evidence to date has been counterintuitive, testing this hypothesis using different samples and different methodologies is important before generalizations can be made. This led to our next hypothesis that H2: There is a relationship between time spent working and academic performance. According to Pinder (1984) and others (Chan, Schmitt, Sacco, & DeShon, 1998; Chatman, 1989; Dreher & Bretz, 1991; Nonis & Wright, 2003; Wright & Mischel, 1987), performance is a multiplicative function of both ability and motivation. Performance = Ability × Motivation For example, a student with very high ability but low motivation is unlikely to perform well, whereas a student with low ability but high motivation is likely to perform well. That is, the variability in motivation across students may dampen associations between ability and performance. In the same vein, one can argue that it is simply the study behavior that ultimately brings about the desired performance and not students’ inner desires or motivations. This is supported by the widely held belief that it is hard work (i.e., time spent on academic activities outside of class by a student) that results in academic success and that laziness and procrastination ultimately result in academic failure (Paden & Stell, 1997). Therefore, similar to how motivation interacts with ability to influence academic performance, one can infer that behavior such as hard work interacts with ability to influence performance among college students. This led us to our third hypothesis to be tested in this study. H3: Behavior (time spent studying outside of class) will significantly interact with ability in that the influence that ability has on academic performance will be higher for students who spend more time studying outside of class than for students who spend less time studying. All indications are that today’s college freshmen are less prepared for college than their predecessors. American College Testing (ACT) Assessment reports that fewer than half of the students who take the ACT are prepared for college. According to the Legislative Analyst’s Office (2001), almost half of those students regularly admitted to the California State University system arrive unprepared in reading, writing, and mathematics. Although these statistics are common at most colleges and universities in the nation, how institutions handle these concerns varies. Strategies include attempting to develop methods to diagnose readiness for college-level work while students are still in high school or requiring remedial courses of entering freshmen, thereby lowering academic standards. There are others who believe that it is not only reading, writing, and mathematics abilities that influence academic performance, but also nonability variables, such as motivation (Barling & Charbonneau, 1992; Spence, Helmreich, & Pred, 1987), self-efficacy (Bandura & Schunk, 1981; Multon, Brown, & Lent, 1991; Zimmerman, 1989), and optimism (Nonis & Wright, 2003). Although a minimum level of ability is required, it is plausible that nonability variables will compensate for ability inadequacies to bring about the required level of performance. One question that interests all parties is whether hard work (i.e., more time spent studying) will influence the relationship between motivation and performance. That is, will the relationship between motivation and academic performance be stronger if a student puts more effort or time into studying outside of class compared with those who put in less time? This led to our final hypothesis, one that was speculative in nature, but nevertheless has implications for both students and academicians. H4: Behavior (more time spent studying outside of class) will significantly interact with motivation in that the influence that motivation has on academic performance will be higher for students who spend more time studying outside of class compared with students who spend less time studying outside of class. METHOD Sample We secured the data for this study from a sample of undergraduate students attending a medium-sized (10,000+), Association to Advance Collegiate Schools of Business (AACSB)-accredited, public university in the mid-south United States. To obtain a representative sample of all students, we selected classes from a variety of business courses (e.g., management, accounting, MIS, finance) ,offered at various levels (freshmen, sophomore, junior, and senior) and at different times (day or night), for the study. Data collection occurred during the 9th week of a 15-week semester. This timing was deliberate because data were being collected for the motivation variable, one that is likely to change among students during the early and late parts of a semester. In addition, information on such variables as time spent on academics or workrelated activities is also likely to vary during the beginning, middle, and end of a semester. We distributed surveys and explained them to those students who participated in the study. The survey consisted of two parts. The first part required students to maintain a journal during a 1week period, documenting how much time they spent on various activities each day of the week (there were over 25 activities listed under three broad categories: academics, personal, and work related). For accuracy purposes, we asked students to complete their journal each morning, recording the previous day’s activities. The second part of the survey contained demographic information, such as gender, age, and race, as well as measures of several other constructs including motivation (only motivation was used in this study). Participants had to provide their social security numbers for documentation purposes. We assured them that their responses would be pooled with others and no effort would be made to evaluate how any one individual may have responded to the survey. We urged students to take the task seriously and to be accurate in their responses to each question. A cover letter signed by the dean of the college of business was included in each student’s journal. We administered 440 surveys, and 288 were returned. Two hundred and sixtyfour of the returned surveys were usable, yielding an effective response rate of 60.0%. Measures We used the social security numbers provided by the respondents to collect university data for the variable grade point average for the semester (SGPA), semester courseload, number of hours completed to date, and ACT composite score. As such, these variables were not self-reported and should provide more validity to the study’s findings. Achievement Striving We used six items from a Spence et al. (1987) Likert-type 1–5-point scale, to measure students’ achievement striving, which we used as a surrogate for motivation. In several prior studies, researchers have used this variable as a measure of motivation (Barling & Charbonneau, 1992; Barling, Kelloway, & Cheung, 1996). The reported coefficient alpha for this scale is high (0.87), and this scale has been used in several other similar studies (Carlson, Bozeman, Kacmar, Wright, & McMahan, 2000; Nonis & Wright, 2003). Demographic Variables Students reported demographic information, such as gender, age, and racial or ethnic group membership, in their journals. Behavior Variables We also used student journal data to determine the time spent outside of class on academic activities like reading the text and lecture notes for class preparation, going over the text and lecture notes to prepare for exams, and completing assignments and homework. The researcher added these items for the week to derive the total amount of time students spent outside of class on academic activities during the week (TSA). Students also reported the time they spent working, as well as the time it took for them to travel to and from work each day, during the given week. These two items were also added to derive the total amount of time students spent working during a given week (TSW). Analysis As Table 1 shows, sample characteristics were comparable to available demographic characteristics of college students in the United States (Statistical Abstract of the United States, 2002). Other pertinent demographic characteristics for the sample were as follows: average age = 23.8 years; majors = 16% accounting, 13.1% business administration, 12.3% finance, 13.5% management, 14.5% marketing, 14.5% MIS, and the remainder “other” business majors. January/February 2006 153 We coded gender and racial or ethnic group membership and used them as dummy variables consisting of two categories (coded 0 or 1), such as male or female and African American or other, because 97.5% of the sample was either Caucasian or African American. To determine the bivariate relationships that the plausible predictor (independent) variables had with the academic success (dependent) variables, we calculated Pearson’s product moment correlation coefficients. Table 2 shows both the descriptive statistics and the Pearson’s correlation coefficients. The achievement-striving measure demon- strated an acceptable reliability coefficient as per Nunnally (1978). Prior to testing the hypotheses, it was important for us to control for variables that were likely to have an impact on academic performance other than the variables that we were testing. Studies have found that demographic variables, such as gender, age, and race (Cubeta, Travers, & Sheckley, 2001; Strauss & Volkwein, 2002), influence the academic performance of college students. Therefore, we tested H1 and H2 using partial correlation coefficients, controlling for the extraneous variables gender, age, and racial or ethnic group. Aca- TABLE 1. Demographic Characteristics of the Sample Compared With the Population, in Percentages Demographic characteristic Gender Male Female Racial/Ethnic Group White African American Other Employment Status Do not work Work part time Work full time Populationa Sample 43.6 56.3 44.2 55.8 77 12.1 11 85 12 2.5 35.6 30.3 34.1 34 28 37 Note. The sample consisted of undergraduate students enrolled in business courses at a mediumsized, Association to Advance Collegiate Schools of Business-accredited public university in the mid-south. a Based on Statistical Abstract of the United States (2002). demic load was also included as a control variable because students who take more courses are likely to spend more time studying outside of class compared with students who take fewer courses. In addition, we treated TSA and TSW as independent variables, and we used SGPA as the dependent variable. We tested moderator relationships proposed in H3 and H4 through moderated multiple regression analysis (Cohen & Cohen, 1983; Wise, Peters, & O’Conner, 1984). We performed three regressions: (a) We regressed the dependent variable (SGPA) on the control variables (gender, age, racial or ethnic group membership, and academic load); (b) we regressed the dependent variable on the control variables, plus the independent variable (i.e., ACT composite score as a surrogate for ability), plus the moderator variable (i.e., TSA as a surrogate for hard work or behavior); and (c) we regressed the dependent variable on the control variables, plus the independent variable, plus the moderator variable, plus the interaction (i.e., ACT composite score and TSA). The process involved conducting three regression models for each moderator hypothesis. This process facilitated the investigation of a potential direct influence of the moderator variables (when they serve as predictors) and the extent to which the posited moderator influence actually exists. When both the independent and the moderator variable are continuous TABLE 2. Descriptive Statistics and Pearson Product–Moment Correlations for Study Variables Variable 1. 2. 3. 4. 5. 6. Gender Age Race ACT composite (ACT) Achievement striving (AST)a Time spent outside of class on academic activities (TSA) vs. academic load 7. Time spent working (TSW) vs. academic load 8. Semester grade point average (SGPA) M SD 1 2 3 — 23.76 — 22.00 3.53 — 6.29 — 3.91 0.71 — –0.04 0.04 –0.01 –0.17* — –0.07 –0.24* 0.21* — 0.31* –0.09 — –0.02 — 12.94 8.57 –0.07 0.34* –0.16 –0.18* 0.29* — 16.84 14.55 0.03 –0.03 0.06 –0.03 –0.16* –0.06 — 2.97 0.76 –0.11 –0.09 0.27* 0.35* 0.05 –0.10 a reliability coefficient = 0.77. *p < .05 (one-tailed). 154 Journal of Education for Business 4 0.45* 5 6 7 8 — (ACT composite and TSA), as in this study, the appropriate statistical procedure to detect interaction is the moderated multiple regression analysis (Barron & Kenny, 1986). Because the measurement units associated with the various scales used in this study were different, we standardized variables investigated in the analy- ses and used z scores when testing hypotheses. RESULTS The partial correlation coefficient between TSA and SGPA, controlling for the variables gender, age, race, and academic load (r = .10, p = .19), was in the TABLE 3. Results of Moderated Multiple Regression Analysis of Time Spent Outside of Class on Academic Activities (TSA), ACT Composite Score (ACT), and Semester Grade Point Average (SGPA) Independent variable Control (Step 1) Gender Age Race Academic load Predictor (Step 2) ACT composite (ACT) Time studying (TSA) Moderator (Interaction) (Step 3) ACT × TSA Slope SE t p –0.12 0.03 0.70 0.05 0.13 0.10 0.22 0.07 –0.90 0.26 3.22 0.74 .36 .78 .00* .46 0.43 0.17 0.07 0.07 6.60 2.54 .00* .01* 0.18 0.07 2.69 .01* R2 .06* .25* .28* *p < .05. SGPA TSA (High) TSA (Low) LOW HIGH ACT FIGURE 1. Time spent studying (TSA) and ACT composite score (ACT) interaction on semester grade point average (SGPA). Graph is based on predicted values (y-hat) generated from the regression equation for individuals 1 standard deviation above and below the mean for TSA and ACT. expected direction, but not significant. Therefore, H1 was not supported. The partial correlation coefficient between TSW and SGPA (r = −.08, p = .28) was also statistically insignificant, failing to support H2. Moderated Multiple Regression (MMR), controlling for gender, age, race, and academic load, provided the statistics required to test the remaining two hypotheses. For H3, the R2 for the control variables was statistically significant (R2 = .06, p < .05). In the second step, the increment to R2 was statistically significant for the addition of the main effects of ACT composite and TSA (∆R2 = .19, p < .05). In fact, the main effects of both ACT composite and TSA were also significant (p < .05). From Step 2 to Step 3, the increment of R2 was also significant for the addition of the interaction term (∆R2 = .03, p < .05); this supported H3, which stated that TSA would interact with ability (see Table 3). Predicted values generated from the regression equation that were one standard deviation above and below the mean for ACT composite score and TSA indicated that students who were high in ACT composite and TSA most likely had a very high semester GPA (y-hat or predicted value = 3.95), relative to students high in ACT composite score with low TSA (y-hat = 3.1) and relative to students low in ACT composite with either high (y-hat = 2.5) or low (y-hat = 2.7) TSA. This is the appropriate technique to interpret interaction terms when moderated multiple regression is implemented (Cleary & Kessler, 1982; Cohen & Cohen, 1983). Results are shown in Figure 1. For H4, the R2 for the control variables was once again statistically significant (R2 = .10, p < .05). In the second step, the increment to R2 was statistically significant (∆R2 = .14, p < .05) for the addition of the main effects of achievement striving and TSA. However, the main effect of TSA was not statistically significant. From Step 2 to Step 3, the increment of R2 was also not statistically significant (∆R2 = .01, p > .05) for the addition of the interaction term. These results did not provide support for H4, which stated that time spent studying outside of class would interact with motivation (see Table 4). Therefore, H4 was not supported. January/February 2006 155 TABLE 4. Results of Moderated Multiple Regression Analysis of Time Spent Outside of Class on Academic Activities (TSA), Achievement Striving (AST), and Semester Grade Point Average (SGPA) Independent variable Control (Step 1) Gender Age Race Academic load Predictor (Step 2) Achievement striving (AST) Time studying (TSA) Moderator (Interaction) (Step 3) AST × TSA Slope SE t p –0.23 –0.07 0.87 0.06 0.13 0.07 0.28 0.06 –1.81 –1.08 4.42 0.98 .07 .28 .00* .33 0.40 0.01 0.07 0.06 6.36 0.18 .00* .85 0.04 0.06 0.58 .57 R2 .10* .24* .24 *p = .05. DISCUSSION We drew the following conclusions from the analyses. 1. Contrary to popular belief, the findings suggest that TSW has no direct influence on SGPA. 2. Based on the partial correlation, findings suggest that TSA has no direct influence on academic performance (measured as SGPA). 3. The main effects of both ACT composite score and achievement striving are statistically significant. 4. In the presence of ACT composite score, the main effect of TSA also has a statistically significant relationship with SGPA. However, in the presence of achievement striving, the main effect of TSA does not have a significant interaction with SGPA. 5. The interaction between ACT composite score and TSA significantly influences SGPA. 6. The interaction between TSA and achievement striving did not significantly influence SGPA. Based on partial correlation coefficients, neither of the hypotheses that tested direct relationships (H1 and H2) was supported. However, one of the hypotheses that investigated the moderator relationship was supported (H3). These results indicate that the relationships that college students’ abilities (ACT composite score), motivation (achievement striving), and behavior (TSA and TSW) have with 156 Journal of Education for Business academic performance are more complex than what individuals believe them to be. One important finding of this study is the lack of evidence for a direct relationship between TSW and academic performance (H2). TSW did not directly affect academic performance. At a time when the percentage of college students who work is at an alltime high and administrators are concerned about its influence on academic performance, these results are encouraging. Although more empirical evidence may be required prior to making any definitive conclusions, these results did not contradict the findings of Strauss and Volkwein (2002) or Light (2001). Contrary to popular belief, both Strauss and Volkwein and Light found that working more hours was positively related to GPA and suggested that students apply the same work ethic to both their academic and paid work (i.e., those who earn higher grades are students who are more motivated, and work harder and longer than others). Perhaps academically strong students are better at balancing academic and job-related work, thereby reducing the negative effects that TSW may have on academic performance. Based on the partial correlation (r = .10, p > .05), the expected influence that TSA has on academic performance (H1) was not supported. When we tested H4, the insignificant main effect between time spent outside of class on academic activities (TSA) and academic performance (see Table 4) also supports the above conclusion. However, when we tested H3, the significant main effect between TSA and academic performance (Table 3) was not consistent with the previous findings in H1 and H4. That is, when ACT composite score was used as a predictor (in the absence of achievement striving), TSA had an impact on academic performance (see Table 3). Also, when achievement striving was used as a predictor (in the absence of ACT composite), TSA did not impact academic performance (see Table 4). In summary, when ACT and TSA were used as predictors, TSA was able to explain variation in academic success that was not explained by ACT (Table 3). However, when achievement striving and TSA were used as predictors, TSA was unable to explain any variation in academic performance that was not explained by achievement striving. Results from H3 show that TSA was a predictor and a moderator in the presence of ACT composite (a quasimoderator). Results suggest the importance of both ability (i.e., ACT composite score) and behavior (TSA) measures in determining academic performance (H3). As indicated by the significant and positive slope coefficient for the interaction term between ability and behavior (slope = 0.18), it is simply not ability alone that brings about positive performance outcomes. Variables such as TSA strengthen the influence that ability has on student performance. At a time when most efforts by administrators and instructors are focused on curriculum and pedagogical issues, this study’s results show the need to also give attention to the composition of today’s college student populations in terms of what they bring to class (i.e., study habits). H4, which stated that the influence that behavior (i.e., TSA) has on academic performance would be higher for students with high levels of motivation than for students with low levels of motivation, was not supported. In this instance, it is clear that, in the absence of ability as a predictor, high levels of motivation or behavior will not bring about the desired academic performance or outcome. Implications At a time when students spend less time studying and more time working, our results provide food for thought, although it may be premature to derive implications from the findings of this study. Should subsequent researchers using different samples validate findings of this study, there are implications for both students and administrators. Students Results from studies such as this can be passed on to students. This can be easily done at a student orientation, in student newsletters, on the Web, or in the classroom. It should be clearly communicated to them that their abilities, motivation, and behavior work in tandem to influence their academic performance. If students are lacking in even one of these areas, their performances will be significantly lower. Once students have a better understanding of how ability, motivation, study time, and work patterns influence academic performance, they may be more likely to understand their own situations and take corrective action. More important, they may be less likely to have unreasonable expectations about their academic performance and take more individual responsibility for its outcome rather than conveniently putting the blame on the instructor. For example, it is not uncommon for intelligent students to believe that ability will result in high levels of academic performance regardless of their level of motivation or effort. The results of this study show the impact of ability on academic performance to be much higher for students who spend more time studying than for those who spend less. Also, the results did not show a direct link between TSW and academic performance. Although this can be an encouraging finding at a time when a large percentage of college students are working longer hours while attending college (Curtis & Lucus, 2001), more research is needed prior to making generalizations. For example, it is plausible that the direct relationship between TSW and academic performance can be moderated by several personal (i.e., ability, motivation, study habits) and sit- uational (i.e., level of stress, courseload) variables, and, as such, the impact that TSW has on academic performance may be different for different student populations under different situations or circumstances. We did not investigate those relationships in this study. Administration Study results also have implications for both the recruitment and retention of students. According to ACT, only 22% of the 1.2 million high school graduates who took the ACT assessment in 2004 achieved scores that would make them ready for college in all three academic areas: English, math, and science (ACT News Release, 2004a). First, university administrators as well as faculty should realize the importance of recruiting students who are academically prepared for college as indicated by ACT composite or SAT scores. Having the motivation or a strong work ethic may not bring about desired performance outcomes in the absence of ability, as evidenced by H4. This can be a potential concern for colleges and universities that have low admission standards (i.e., low ACT or SAT score requirements and lower acceptable high school GPAs) or open admission policies. Due to low admission requirements, these institutions are more likely to have a larger percentage of students who lack the minimum ability needed to succeed in college compared with a smaller percentage of such students in colleges and universities that have high admission standards. Therefore, colleges and universities that have relatively low admission standards need to have a process in place to identify those students who lack the necessary abilities (e.g., quantitative skills, verbal skills) to succeed in college and provide them with ample opportunities to develop those abilities while in college by offering remedial courses. Failure to develop those abilities prior to taking college-level courses can be a recipe for poor academic performance and low retention rates. Data compiled by ACT show a strong inverse relationship between admission selectivity and dropout rates: Highly selective = 8.7%, selective = 18.6%, traditional = 27.7%, liberal = 35.5%, and open = 45.4% (ACT Institutional Data File, 2003). Also, on the basis of the results from H3, students with high ability who also spend more time studying are the ones who are most likely to excel in college as indicated by their GPA (Figure 1). These are the type of students who are most likely to perform well academically and bring universities as well as individual programs a high-quality academic reputation, and, as such, a process should be in place to recruit and retain them. In addition to recruiting, retaining the students and helping them to achieve their goals is an important issue for institutes of higher education. Research results indicate that just over half of students (63%) who began at a 4-year institution with the goal of a bachelor’s degree have completed that degree within 6 years at either their initial institution or at another institution (U.S. Department of Education, 2002). Unfortunately, an alarming number of schools have no specific plan or goals in place to improve student retention and degree completion (ACT News Release, 2004b). This shows the need for institutes of higher education to have their own models to precisely predict and track the academic performance of their prospective students to ultimately monitor and control student retention and dropout rates. Although measures of ability such as ACT and SAT scores and high school GPA are widely used for college admission and GPA at college is used to evaluate the progress of the student, the results of this study show that, if included, nonability variables such as motivation and TSA may significantly improve these prediction models. This information, if collected and monitored, would be useful in terms of decision making for university administrators as well as faculty. Limitations and Direction for Future Research We made significant efforts to minimize measurement error in variables that are normally self-reported, such as ACT composite scores and academic performance (GPA), as well as those variables that rely on memory of past events, such as TSA and TSW (i.e., a question such as time spent studying in a given week or time spent studying January/February 2006 157 the previous week). By using university data for variables such as ACT composite scores and academic performance as well as collecting the time data based on a diary maintained by participants during a 1-week period, we minimized measurement error. Nevertheless, although results can be generalized to the university where we collected the data, additional evidence will be required prior to generalizing statements to all university settings. In this respect, a national sample that investigates these relationships can either support or refute this study’s findings. The study did not include a variable that measured the effectiveness level or quality of the time students spent studying, which may be one reason why H1 was not supported. It is very likely that both the time that students spend studying as well as how this time is spent should be measured. That was certainly a limitation of this study. Results of future studies in which researchers include this variable (i.e., time management perceptions and behaviors measured by Macan, 1994; Macan, Shahani, Dipboye, & Phillips, 1990) will provide more insight into this issue. If TSA moderates the relationship between ACT composite and academic performance, it is plausible for TSW also to moderate the relationship between ACT composite and academic performance. Therefore, in a future study, researchers might investigate whether the relationship between ACT composite score and academic performance is stronger for students who spend less time working compared with the students who spend more time working. We did not investigate these relationships because they were outside the scope of this study. We limited the personality variable under investigation to achievement striving. Other variables such as optimism and self-efficacy are likely to influence academic performance, and future studies will be able to address these issues in more depth. However, in this study, we addressed an important concern of the academic community at a time when such empirical research is not widely available, and, as a result, we 158 Journal of Education for Business have contributed to the higher education literature. NOTE Correspondence concerning this article should be addressed to Sarath A. Nonis, Professor of Marketing, Department of Management and Marketing, Box 59, Arkansas State University, State University, AR 72467. E-mail: snonis@astate.edu REFERENCES Ackerman, D. S., & Gross, B. L. (2003, Summer). Is time pressure all bad? Measuring between free time availability and student performance perceptions. Marketing Education Review, 12, 21–32. ACT Institutional Data File. (2003). National collegiate dropout and graduation rates. 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January/February 2006 159 Studies in Higher Education ISSN: 0307-5079 (Print) 1470-174X (Online) Journal homepage: http://www.tandfonline.com/loi/cshe20 ‘It’s almost a mindset that teachers need to change’: first‐year students’ need to be inducted into time management Jacques van der Meer , Ellen Jansen & Marjolein Torenbeek To cite this article: Jacques van der Meer , Ellen Jansen & Marjolein Torenbeek (2010) ‘It’s almost a mindset that teachers need to change’: first‐year students’ need to be inducted into time management, Studies in Higher Education, 35:7, 777-791, DOI: 10.1080/03075070903383211 To link to this article: https://doi.org/10.1080/03075070903383211 Published online: 31 Aug 2010. Submit your article to this journal Article views: 2578 Citing articles: 28 View citing articles Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=cshe20 Studies in Higher Education Vol. 35, No. 7, November 2010, 777–791 ‘It’s almost a mindset that teachers need to change’: first-year students’ need to be inducted into time management Jacques van der Meera*, Ellen Jansenb and Marjolein Torenbeekb aHigher Education Development Centre, University of Otago, bUniversity of Groningen, Groningen, The Netherlands Dunedin, New Zealand; jacques.vandermeer@otago.ac.nz Studies 10.1080/03075070903383211 CSHE_A_438499.sgm 0307-5079 Original Society 02010 00 Dr 000002010 Jacquesvan in for Article Higher (print)/1470-174X Research derEducation Meer into Higher (online) Education Taylor and Francis Ltd This article discusses the findings related to a number of research projects investigating students’ expectations and experiences of the first year in higher education. In particular, findings with regard to first-year students’ expectations and challenges with issues of time management are reported. It was found that many students were realistic about having to plan their work independently, and having to spend a good amount of their time during the week on self-study. However, many students found it difficult to regulate their self-study and keep up with the work. They were also not always sure how they were to organise their self-study time. It is argued that universities could and should play a more active role in helping first-year students to make sense of time management. Keywords: academic acculturation; self-directed; student transition; study habits; student workload Introduction It is widely acknowledged that the students’ experiences during their first year in higher education are of enormous importance for students’ commitment to return for the second year and completing their degree (Krause et al. 2005; Pascarella and Terenzini 2005; Upcraft, Gardner, and Barefoot 2005; Yorke 1999; Yorke and Longden 2007). As more students have entered higher education retention of first-year students and degree completion have become important foci of attention. As most students who leave higher education do so during or immediately after the first year, students’ firstyear experiences have been identified as of being in need of particular attention. Furthermore, students’ engagement with universities has changed as a consequence of technological developments and increased numbers of students who work or are enrolled as part-time students. The massification and diversification of higher education have raised questions of whether approaches to learning and teaching need to be rethought, and whether ‘traditional’ higher education pedagogy is still appropriate. It has been suggested that changes in students’ engagement with university studies, and the more diverse backgrounds of first-year students, need to be recognised in the organisation of the learning environment and student support (James 2001; Kantanis 2000a; Krause 2005a, b; McInnis 2001). *Corresponding author. Email: jacques.vandermeer@otago.ac.nz ISSN 0307-5079 print/ISSN 1470-174X online © 2010 Society for Research into Higher Education DOI: 10.1080/03075070903383211 http://www.informaworld.com 778 J. van der Meer et al. Although an argument can be made for reconsidering tertiary education pedagogy in general, reconsidering approaches to first-year teaching and learning is of particular importance. One of the central issues is whether universities acknowledge a role in helping students to get used to learning and teaching environments that are different from those at high schools. Prebble et al. (2004), in discussing student support, distinguish between discourses of assimilation and adaptation: are students expected to assimilate into universities, or should universities adapt to the students who enter universities? McInnis, James, and McNaught (1995, 3) argue in favour of institutional responsibility for helping students to adapt: ‘The central problem for teaching and learning in the face of increasing diversity in the student population is that of aligning institutional goals with individual needs’. They add that this does not mean that the first year should be without intellectual challenge: ‘universities and academics have a responsibility to respond to the problematic nature of the transition process, especially in the face of the wider range of student abilities and experiences following the rapid expansion of the higher education system’ (3). In recognition that the first year, and, more specifically, the first weeks or months in the first year, is a distinct period for students, many universities have developed specific transition programmes. Universities and colleges in the USA have a long history in these types of programmes (see, for example, Barefoot 2000; Gardner, Barefoot, and Swing 2001; Swing 2003; Upcraft, Gardner, and Barefoot 2005). In Australia these programmes are also increasingly part of universities (see, for example, Adonis 2000; Asmar et al. 2000; Clulow 2000; Kantanis 2000b, 2001). In New Zealand, although there are some examples of institution-wide transition programmes (Mason-Rogers 2002), there are few published sources on how these orientation programmes work. In the Netherlands most universities have induction programmes to learn about the university; these programmes include orientation to the faculty and the social environment of the city. However, the impact of those programmes on the student experience has rarely been investigated. One transition challenge for first-year students is related to effective time management and self-study skills for the particular university teaching and learning context. Time management and self-study, for the purpose of this article, will be understood as part of the same set of skills related to organising and keeping up with a range of study tasks. A study by the UK Higher Education Academy (Yorke and Longden 2007) clearly suggested that students experienced challenges in managing their time. Krause and Coates (2008), in considering the results of a student engagement instrument, commented that being able to ‘manage one’s time, study and strategies for success as a student is foundational to success in the first year’ (500). Furthermore, time management skills are important contributors to study success (Macan et al. 1990). Confidence in long-term planning is a particularly important predictor of successful study performance (Trueman and Hartley 1996). Furthermore, students’ time allocation skills have been proven to be related to the acquisition of discipline-specific and generic competencies (Meng and Heijke 2005). In this article we will report on research findings from different projects related to first-year students’ experiences in two different institutions. The central questions we discuss are: (1) How prepared do students feel in time management and self-study skills before they enrol in university? (2) What do first-year students report as challenges in the area of time management and self-study? Studies in Higher Education 779 Research projects and development This article outlines the development of an international research collaboration, as well as reporting on the findings of the different projects. Chronologically, the interviews and survey at the University of Otago preceded the development of the Readiness and Expectations Questionnaire (REQ). The survey and interviews sought to establish first-year students’ academic challenges in their first semester. The initial findings of the survey and interviews were presented at the first European First-Year Experience Conference in 2006 (van der Meer 2006). The presentation at the conference led to discussions about whether students’ challenges with, for example, time management resulted from unrealistic expectations, their levels of preparedness or whether universities are not very good at making clear what is expected of students. We concluded that not much was known about students’ expectations and sense of preparedness before starting their studies at university. The result of these discussions was the beginning of a collaborative project between two universities in two different countries to investigate these issues further. We agreed to develop a new instrument. The advantage of these two particular universities collaborating was that we could develop this instrument relatively fast: as the start of the academic year in the Netherlands and New Zealand is at different times of the year, we could trial new versions twice a year. Interviews with first-year students were also conducted at the University of Groningen. The interviews were part of a project on the transition from secondary education to university. In this project the interviews were used to interpret the survey results. In presenting the interview data, therefore, a much smaller section provides some illustrative comments from this study. The intention is to demonstrate that students in both institutions commented in very similar ways about similar issues. As we seek to demonstrate that the issues around first-year students’ time management seem to be similar in different countries, we present the quantitative data of the Readiness and Expectations Questionnaire first, and then elaborate on the richer qualitative data from students’ interviews and free-text survey comments. Data and methods To address the questions above we used different data sources: (1) Readiness and Expectations Questionnaire (REQ) data collected in both the University of Otago and the University of Groningen; (2) interview and survey data collected in the University of Otago by the first author (van der Meer 2008); and (3) interview data collected in the University of Groningen by the third author (Torenbeek). The REQ measures expectations and readiness in a range of fields that were derived from an extensive body of research on the first-year experience. Students were asked to answer a number of categorical questions and a range of questions on a five-point Likert-scale (from ‘strongly disagree’ to ‘strongly agree’). These Likert-scale questions related to respondents’ expectations of what would happen in the first year at university and their self-perceived readiness for university. The items were composed around several dimensions, including induction and time management. Expectationrelated questions included items such as ‘I will have to do a lot of independent research tasks’. Preparedness questions included items such as: ‘My previous experiences prepared me well to keep up with a lot of readings’ and ‘I am good at working independently’. 780 J. van der Meer et al. In July–August 2006 the REQ was first administered at the University of Groningen. The survey was sent to students before they started their studies. Based on the analysis of that data (Jansen and van der Meer 2007a, b), some items of the questionnaire were reformulated for the administration at the University of Otago in March 2007. In July–August 2007 a slightly adapted version was administered at the University of Groningen and in March 2008 a further adjusted (much reduced) version at the University of Otago (van der Meer and Jansen 2008). Because this instrument was developed in an international collaborative project, we were keen to find out whether students in two countries with different educational systems differed in their expectations and perceived readiness. In this article we report on the data from the Dutch 2007 and New Zealand 2008 surveys (n = 1465 and n = 440 respectively). The other survey, which was only conducted at the University of Otago, was administered in 2004 amongst students enrolled in 100-level courses (n = 1967). New Zealand universities distinguish between 100, 200 and 300 level courses; these roughly, but not necessarily, equate to courses followed in the three undergraduate years. To complete an undergraduate degree, students need to have completed a prescribed number of these three different levels of courses. Students can complete some of the lower-level courses in higher year levels. For the purpose of this project, such students were excluded from the analysis. Four open-ended questions were asked that related to students’ overall academic experiences in the first semester, such as ‘what advice would you give future first-year students?’ Interviews were conducted in both institutions. The University of Otago interviews were conducted with first-year students (n = 27) in the same year (2004) as the survey. Similar to the survey, the interviews were trying to find out more about first-year students’ academic experiences during the first semester. Interviews were conducted at the University of Groningen in 2008 for a project about the transition from secondary education to university in order (among other things) to identify similarities and differences in study approach at the two levels of education. Twelve first-year students from a range of disciplines participated in the interviews, which took place in the second part of the academic year. A data-mining approach was used to interrogate the three data sources (Castellani and Castellani 2003). Data mining in the context of this article will be understood as the selective use of data sources with particular research questions in mind. For the purpose of this article data related to time and study management was ‘mined’. Results from the REQ In the two rounds of administering the survey in the two countries, various items were deleted and added to develop an instrument with good reliability scores on the various scales. Of the common questions, the differences between the Groningen and Otago responses were insignificant (p > .001) for 21 out of 39 questions. There were, however, some differences between the two universities on the scale means. This could be possibly explained by sample size differences. As the means do not differ in an opposite direction (that is to say that, for instance, the Groningen students scored below 3.0, and the Otago students above) these differences were not deemed relevant for this article. For this article we are interested in the time-management scale, the induction scale and the similarity with high school scale. Table 1 shows the reliability coefficients (alpha), means for these scales, and the significance of the differences between the means for Otago and Groningen cohorts (independent samples t-test). The ‘time Studies in Higher Education Table 1. 781 comparison of scales. Scales Expectations Induction Similarity with high school Time management Items 4 5 4 UoO* UoG** UoO UoO UoG UoG Mean Diff. α α n mean n mean diff. Sign. .74 .66 .72 .70 .63 .72 440 429 403 2.46 1465 2.64 2.29 1454 2.00 3.63 1385 3.73 −0.18 .000 0.29 .000 −0.10 .018 *UoO = University of Otago; **UoG = University of Groningen. management’ scale contained items directly related to managing time, as well as items that related to planning and independent work. The scores for this scale suggest that students in both countries felt well prepared to work more independently once at university. For both groups of students there was a significant correlation (p < .001) between expectations of induction and expectations that high school and university would be similar. While Groningen students seemed to have higher expectations of induction, they also considered the similarity between high school and university to be less. For the Otago students there was a lesser expectation of induction and slightly higher expectations of similarity with high school. The survey also asked students to indicate how much time they anticipated spending on the various activities. Otago students expected to spend on average 19 hours per week on class-related activities, and 20 on independent study; for Groningen these were 18 and 17, respectively. Overall then, it could be suggested that there were no major differences between the first-year students in the two countries with regard to their sense of preparedness and expectations of higher education on the dimensions included in the survey. The data also suggest that, overall, students understood that university would be different from high school, that they would have to do much of their time planning themselves, and that they considered themselves confident that they would be able to manage their time. Lastly, students were reasonably realistic about the amount of time to be spent on academic activities. Students’ responses to the survey questions of the Otago UNI101survey and the interviews, however, revealed that some students experienced significant challenges. Results from the survey and interviews in the Otago studies Students highlighted different aspects related to time management and self-study (or independent study) challenges. One of the main reported concerns was keeping up with the study requirements. Others pointed to the particular challenge of first-year students to understand the new demands, the level of independent study required, the seeming lack of assistance in their transition into more independent study at university, and understanding the study and time management expectations. The following sections will further illustrate some of these challenges, interweaving data from the interviews and surveys. Keeping up with study In the 2004 survey, responses to the question about what respondents would advise future first-year students, it was clear that time management was a considerable 782 Table 2. J. van der Meer et al. Comments related to time management. Nature of comment Keep up, stay on top of work, don’t fall behind Start early Pay attention to time management Revise all the time Work all the time Total number of student comments related to time management Number of comments 164 100 22 19 13 318 concern. Of the 1179 students who responded to this question, 318 students commented on time management issues. Table 2 shows the different categories of responses. The common theme was advising future students to start working from day one, and to work consistently throughout the semester. Some students specifically mentioned the importance of time management. The top category of ‘keep up’ comprised comments such as: ‘keep up with work, don’t leave everything to last minute – workload gets too much and catch up gets impossible’, ‘keep up with readings’, ‘stay on top of work, do assignments early, go to classes, do the readings before lectures’, and ‘try not to fall behind, ask questions when you need help’. Survey comments related to the other categories included some of the following comments: ‘start working early in the year as otherwise you will drop behind and all of a sudden it will be the exams and you will be wanting to leave’, ‘revise from day one, SERIOUSLY!! [sic] Otherwise it’s really hard’, and ‘work all the time to make you understand everything at the time’. One respondent mentioned attending a workshop on the issue: ‘make sure you go to a time management workshop at the student learning centre’. Responsibility for keeping up and remembering There were few indications, however, that students considered the university as having a responsibility in the area of self-study planning and time management. One of the interviewees, Natasha (a pseudonym), for example, commented: ‘I don’t think anyone really gives you a lot of help with that. It’s just you’re just expected to figure out a lot of things like that’. Some survey respondents suggested the university could provide some help: ‘not a single mention of upcoming essays/assignments. First years need some degree of spoon-feeding after seventh form [final year of high school in New Zealand]’ and ‘lack of info on how to prepare earlier’. Where the topic of students struggling with their workload came up in interviews, the initial reaction of some students was to blame themselves. The assumption seemed to be that the university as an institution was not expected to play a role in this. It was only when an interview question was phrased in such a way that they were invited to think of other perspectives that interviewees considered alternative explanations. Pamela, for example, initially put the responsibility on students themselves: ‘I guess it’s easier to say they [university] could do this and do that, but really, most of it’s on your shoulders whether you do it or not. I don’t know how you can change that’. She then wondered if there was something that could be done, like in class: ‘I guess time management would be, I don’t know if they, they probably do run tutorials’. Studies in Higher Education 783 Although few students directly suggested that the university had a responsibility in students’ time management, some ideas were mentioned of how teachers could be of help. Susan had come to realise during the semester that she should have started much earlier with some of her work. She felt that ‘people need to know that … realising that’. She implied that it would have been helpful if they were told. As far as she herself was concerned, she had learned a lesson through experience: ‘I know next semester will go better because I now know, [to] get on top of my work straight away’. Some students also considered it helpful to be reminded of upcoming assignments. This can be seen, for example, in the following survey comments: ‘give notice in lectures [of] upcoming assessments’ and ‘mention assignment due dates in lectures’. One survey respondent also seemed to suggest that this would be a caring thing to do: ‘reminders of important dates etc, helpful, caring, personal’. One interviewee, a school-leaver, very soon realised that university was different from high school. She felt that teachers at university should emphasise a bit more the short time frame for most courses. Sandy commented on one course, where they were reminded by teachers and through Blackboard (a proprietary electronic course management programme): ‘generally they do remind you yeah they’re pretty good’. Veronique too, had some good experiences of being reminded. She thought it was particularly important to be reminded about the importance of time management in courses where students had to achieve a certain grade point average for admission to their preferred second year course: ‘I think they need to keep on reminding people that the first year is what determines you to get into second year and so, rather than the first year being a muck around year, it almost needs to be the year that you work the hardest, so you can get into what you want’. She then added that ‘It’s like almost a mindset that they [teachers] need to change’. What she seemed to suggest was that first-year students are not used to thinking in terms of regular work if there is no immediate deadline looming. Evelyn provides a good illustration of a student who started to make sense of time management and assessment as the first semester progressed. Her first experience was that she nearly forgot she had a test: ‘I didn’t really know about the test until the day before but it wasn’t a problem … it was a Tuesday they told us, they reminded us’. She had remembered that they were told at the beginning of the year, but it had escaped her as the weeks passed. She then mused on the issue of having to be responsible for remembering when assessments take place: ‘Because they don’t sort of tell you at uni. Like remember this, remember that. It’s all up to you to realise that you’ve got to do your own work’. When she came across her course overview, she realised that all the details were in it, and she started her time management by putting all the dates on a time planner: ‘At first I didn’t really read through any of the stuff but then when you do read through it, you sort of use the information quite well’. Independent study The level of independent study required, and the seeming lack of assistance to make the transition into more independent study at university, was another theme. One survey respondent made a comparison with the type of learning expected before coming to university: ‘different compared to school, a lot more independent work – not always knowing exactly what is required in your work’. One of the interviewees, Emily, was somewhat surprised that they were being left so much to study without specific guidance. The lack of direction left her uncertain. She wondered whether this related to a particular teaching style at the university: ‘I just thought you’d spend more 784 J. van der Meer et al. time within the university being taught rather than so much left to yourself’. One survey respondent seemed to appreciate that they had to be independent learners, but also pointed out that first-years have particular needs: ‘for first year students it’s important to be integrated into self-teaching slowly, instead of just left to try and attempt it with no advice guidelines’. This respondent seemed to suggest that there was nothing wrong with a different way of doing things, but that first-year students needed to be assisted in making sense of how things at university worked. Brad, however, did not seem to expect the university to play a role. Although he himself struggled to get clarity on how much to study, and how to approach his study, he thought that it was up to each student: ‘I thought well maybe it’s sort of down to your own personal beliefs on what you should do and what you shouldn’t do, due to the fact that you’re at university and it’s, you know you’re there to learn, if you want to learn you learn and if you don’t you don’t’. Workload expectations Students also experienced more specific challenges related to workload expectations. Students’ awareness that they had to do a lot of work did not necessarily translate into knowing how to approach the study load. Linda, for example, had appreciated that there was going to be a lot of work, but did not know what that meant in practice: ‘at the end of the semester we all panic, freak out … it’s during those times that when you look back you said ahh how I wished I did this, or that or that, but just no one really tells you how to do it’. She did not try to suggest that there had been a lack of information on workload: ‘Like they will say there’s going to be lots of work etc, and you just have to start from day one, but nobody really gives you the idea of what is expected of you’. She seems to suggest that being told about the quantity of work does not translate into workable knowledge on what this means, and how to translate this into planning her time. It is interesting to note that this was a mature student in her mid-twenties who had already completed a bachelor’s degree in Australia. Linda’s experience was echoed by some other students doing a similar course. They had heard that it was going to be a lot of work, but found it surprising that they were asked to do so little. They had expected that they would be given weekly work, or assignments to be done at home. They did not count the weekly exit tests in laboratories as ‘real’ assignments, because they were in the laboratory anyway, and did not have to take the assignment home. One health-science student, Candy, ‘sort of’ knew that she probably had to do more than she did, but was not sure how to go about organising her self-study: ‘some Tuesdays I have nothing from 9 till 3, so it’s kind of like the whole day I’m doing nothing, and it feels like I’ve got no work to do but I guess we’re just supposed to read through our text books, it kind of freaks me out’. She had thought that ‘[course A] would be like a lot of work you know, like they give you a lot of work like you have to do a lot of assignments or whatever, and they haven’t like’. Candy’s comments, and those of other students, could suggest that only take-home assignments were equated with study expectations. Revising and studying towards examinations were not necessarily considered as something they should be doing throughout the semester. Evelyn’s remarks about ongoing study also seemed a good illustration of this: ‘A lot of people, my friends have a lot of work that they’re doing but it’s just it is optional at university, like they’re not going to tell you off if you haven’t done your homework, it’s just a good idea to do work’. Evelyn’s idea was that study at home was optional, unless it was an assignment. It was not obvious to all Studies in Higher Education 785 students, then, how they were supposed to conceive of the time that was not spent on assignments or in classes. Teachers’ expectations Participants in this research reported variable attitudes of teachers in communicating course expectations. Kate commented that teachers in one course had been very clear about the time required for a particular assignment: ‘they told us it would be about 30 hours outside of class. They told us about two, three weeks before, and they reminded us every time they saw us’. When another interviewee, Harriett, contemplated what had been helpful for her, she remarked that: ‘I find it helpful where they’ve basically just said like you know we’ve got four hours of lectures, but I expect you to be doing another four hours out of lectures … give you sort of mainly just a timeframe’. Although the University of Otago equates each course with a particular weekly workload, including both class time and self-study, there were few indications that students had an understanding of this. Although few participants mentioned specific time expectations, some students were given ‘broad’ indications that they had to do some study outside of classes. However, these indications were at times a source of confusion. Teachers referred, for example, to ‘extra’ readings, or exercises that were ‘optional’ or ‘recommended’. Sometimes doing something ‘extra’ was offered as a suggestion rather than requirement, such as Evelyn’s account of what one of her teachers said: ‘when you have a spare bit of time you can look through the notes, or there’s an extra website here you can have a look at if you’ve got some extra time’. Rita commented on one of her lecturers who did remind them every now and then of what they could ‘study up’ about: ‘Like every now and then the lecturer might say “you guys should be looking at this, studying up on this”’. Sometimes teachers were perceived to be saying one thing, but meaning something else. For example, Sandy, who studied one of the modern languages, found it difficult to establish how much studying was expected from her: ‘she [the teacher] said, you know, we can choose to do as much as we want, we would like. But really she does actually expect you to do it’. In this case Sandy perceived a mismatch between the articulated expectation (there is a choice), and what was ‘really’ expected (do it all). Emily hinted at a similar mismatch between what they were told and what it meant. In one of her courses, she said, they had told students: ‘we recommend you do this, we recommend you do that, and nothing’s actually told you should do this. And when it comes to the exams you should have done it, but they’ve never really said so and so that’s kind of a big issue’. These examples, too, suggest that teachers’ intentions were not always clear to students. In summary, what comes through the data is that some teachers seemed to understand that first-year students need more guidance (e.g. regular reminding of upcoming assignments) and others did not. In other words, the issue is not about ‘contradictory evidence’ with respect to students’ challenges, but about variability in teachers’ awareness of the needs of first-year students (as reported by students). Findings from the Groningen interviews Similar to the Otago interview findings, many students at the University of Groningen experienced a distinct difference between high school and university time management. One student, for example, emphasised that: ‘Different from high school we are 786 J. van der Meer et al. not reminded what to do … that doesn’t happen here … you have to do it all yourself’. Another student mentioned that ‘The biggest difference is the level of independence in managing your studies, your time planning, sorting out time tables, what you have to read etc’. Consequently, one student identified that ‘[the important skill to develop is] is independent study skills, that is very important, otherwise you won’t make it I think’. However, some experienced a degree of support from their teachers: ‘I find that in medicine they do seem to guide you. They are keen to point out what to learn, and when to do it by, just like at high school … I find it really clear, I hadn’t expected that, I thought you would be left to your own devices’. Discussion The responses to the Otago survey question about what respondents would advise for prospective first-year students suggested that time management and study-related issues were a concern in the first semester. Interview data in both the Otago and Groningen studies confirmed this. Responses to the REQ survey suggested that students had realistic expectations about the time to spend on their study; they also felt ready for more independent study before they arrived at university, but apparently the reality at university seemed different for them. Considering the findings as a whole, we want to suggest that students’ struggle with time management has less to do with being unrealistic about university expectations or students’ unwillingness to work hard, but more with a lack of understanding how to organise their study at university. This lack of understanding, we would argue, cannot necessarily be attributed to lack of high school preparation, but could also be considered as a lack of appreciation by some university teachers as to the need of firstyear students to be appropriately inducted into what is expected of them, and how to organise their study in a university environment. Issues of time management and self-study have been highlighted in many other studies on first-year students. For example, Kantanis (2000a), in a large study with some 1600 students at Monash University, found that 38% had not come to terms with independent learning after the first semester. Other studies have also highlighted first-year students’ concerns with time-management and self-study issues (Haigh 1999; Maguire 2001; Prescott and Simpson 2004; Ramsay, Barker, and Jones 1999; Sidle and McReynolds 1999; Smith 2003). Haggis (2006) emphasises that problems with organisation of time and study can affect all types of students. Lowe and Cook (2003), in their study with first-year students at the University of Ulster, reported that 21% of the students at the end of two months had experienced greater difficulty with self-directed learning than they expected, whilst a third reported that they were experiencing some difficulties with this. The results of the first-year experience study in the UK (Yorke and Longden 2007) clearly indicate that time management was a concern. In both the open questions asking students to mention the worst feature of their experience, and features they thought should be changed, time management was the leading category. Concerns related to time management and self-study from first-year students’ perspectives, then, were by no means unique to this study. Whereas high schools often have set expectations for students about when students have to complete certain tasks, and when particular milestones have to be met along the way, students experienced this as less so at university. This was especially the case with regard to tasks for which there was no direct assessment component. This was, Studies in Higher Education 787 for example, the case where students were given suggestions for reading, or problemsolving exercises. Another task that students seemed to have little comprehension of was allowing and planning time for ongoing revision towards the examination period. The many blocks of unscheduled time between scheduled class times were a new experience for many students. Some interviewees, for example, reported that they felt they should do work all the time, but did not seem sure how to organise a routine; and some ended up not doing much at all. One interviewee blamed herself towards the end of the semester for not having started revision of material earlier. In some of her courses the assessment was heavily weighted toward the final examinations. Another interviewee had mentioned that she was surprised early in the semester that there was so little work to do; she had expected it to be more difficult. These experiences suggest that it was not obvious to all students that things were done differently at university. For some students, realisation happened as the semester progressed. Students’ report of the manner in which some teachers communicated may have contributed to students not being clear on how to make sense of study expectations. The seemingly ‘optional’ nature of some activities, such as ‘extra’ readings, was experienced as confusing. Advice from staff at times seemed too tentative or indirect for students to apprehend their importance. Where first-year students had to come to terms with a range of demands on their time, seemingly discretionary activities may, therefore, not have been appreciated as important. Amongst these apparently discretionary activities in some courses ‘readings’ figured prominently. Another reported difference with high school was the lesser emphasis on reminding students about forthcoming tasks and deadlines. Some students, for example, mentioned that this had a particular impact on assignments and examination revision. This often resulted in last-minute work. A comment from one interviewee suggested that students had had assessments sprung on them. At the University of Otago, all courses are obliged to inform students at the beginning of the semester how and when they will be assessed. It would, therefore, be unlikely that assessments were completely sprung on students. A number of students mentioned that they probably had been told of assessments, but they assumed this was at the beginning of the year. This could suggest that at that moment the student had not realised that they had to take note of that as being relevant and important. One particular interviewee, for example, started to put assessment dates on her wall planner after she had some earlier experiences of being surprised by upcoming assessments. This student also commented that she had realised that at university students were not told. This seemed to be something she had come to realise over time, rather than something she knew at the start of the year. Students’ experiences in some courses suggest that some teachers may have been aware of first-year students’ needs in respect of time management and study organisation. In some tutorials and laboratories, for example, students were given weekly Blackboard tests or weekly homework. Whether this was done intentionally in recognition of students’ needs could not be established. However, these weekly routines gave students a sense of being on track. Practices of this kind were found helpful by students, and would be worthy of consideration by other teachers. The quantity of academic readings in some of the courses presented students with another challenge that they had not encountered to the same extent at high school or elsewhere. The survey results suggest that many students were challenged by the amount of reading suggested in the first semester. Their advice to incoming first-year students further suggested that they considered this ‘just’ to be a matter of ‘keeping 788 J. van der Meer et al. up’. Perhaps they did appreciate that there are different ways to approach reading, and that being provided with a reading list does not necessarily mean that they had to read everything from start to finish, and ‘know’ everything. One of the interviewees, for example, described how she was overwhelmed by the process and amount of reading. Maguire (2001) remarks that one factor that negatively influenced students’ confidence was not being able to do all the set readings. First-year students in their study were surveyed early in the year. At that point in time they were confident that they would be able to manage their time with regard to reading. When they were resurveyed at the end of the year, there was a remarkable loss in confidence. This, Maguire says, suggests that these students ‘need to adopt a more strategic approach to learning tasks, with an emphasis on time management and effective reading strategies, in order to succeed’ (2001, 103–4). Students, however, cannot be assumed to ‘just’ understand what a strategic approach to readings means. These challenges for first-year students cannot be underestimated. Thomson and Falchikov (1998) identified time management in the context of assessments as one of the main concerns that came out of their surveys and interviews with students. Time management was a concern for all their interviewees. They pointed out that ‘Early in their university career the students may not yet have “tuned in” to the institution. Learning the ways of the institution requires considerable time and effort’ (387). This ‘tuning in’ could be an apt description for some of the problems experienced by some of the interviewees. The gradual awareness of students as the semester progressed that they had to organise themselves differently could be considered a ‘normal’ process that students eventually come to grips with. However, when students come into the university they have to get used to, or adjust to, a wide range of new practices and environments, both academic and social. Nearly two-thirds of students, in the case of Otago as well as Groningen, will for the first time have a much higher level of autonomy and/or live away from home. This can be a considerable upheaval. Some students indicated that pressures to make friendships and integrate into new social environments, such as residential colleges (halls), occupied much time and energy. At the same time, first-year students were expected to learn, or further develop, skills of time management and self-study. There was very little time for them to do so in the short period of a 13-week semester, with assignments often due within the first four weeks. Interviewees’ accounts of having difficulty keeping up, or being surprised by assignments, therefore, are understandable. Conclusion Time management and self-study are clearly challenges for first-year students. It was also clear that this was not always appreciated by teaching staff. The authors of the UK study into the first-year experience (Yorke and Longden 2007, 2008) seemed of the opinion that time management difficulties are not something that universities should have to take responsibility for. In categorising the open question responses, time management is placed under ‘student responsibility’, rather than ‘institutional responsibility’ or ‘dual responsibility’. Although it could be argued that students have the ultimate responsibility to plan their time and study in an effective way, we argue that universities have an important role to play in assisting students to develop the required skills. Students cannot be expected to apprehend straight away what teachers expect from students, what skills students are meant to employ to, for example, read ‘around’ a subject, and how to Studies in Higher Education 789 effectively respond to the changed teaching and study time patterns in the context of a semester. Even where staff generally would consider that students’ time management and self-study skills had nothing to do with them, it could be argued that the particular context of the first semester of the first year are exceptional enough to warrant special consideration by staff. Kantanis (2000a, 6) remarks that ‘given the very nature of transition – that it is a process of adjustment requiring the passage of time – to expect first-year students’ transition to take place speedily is a contradiction that causes considerable consternation for first-year students’. Considering the challenges first-year students experience in managing their time, and the considerable transition students go through on entering university, we argue that teaching and other support staff should play an active role in helping students to make sense of the expectations related to time management and self-study. Furthermore, teaching practices or curriculum planning that recognise the time planning challenges for first-year students can affect students’ time allocation. For instance, Jansen (2004) and van der Hulst and Jansen (2002) showed positive effects of organisational actions that directed students to regular study efforts on study progress. The University of Groningen has highlighted the importance of the first year by allocating a reasonable amount of money to improve the first-year pedagogy, for instance to increase the number of contact hours, to stimulate small group teaching and to stimulate students to regular study behaviour. Virtual learning environments, such as Blackboard, offer great opportunities to support students in their journey in the first year at university. References Adonis, C. 2000. An evaluation of students’ perception of a multi faceted support programme in a large, diverse first year class. 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