DIFFERENCES IN SELF-REGULATION FOR ONLINE LEARNING BETWEEN FIRST- AND SECOND-GENERATION COLLEGE STUDENTS

Research in Higher Education, Vol. 45, No. 1, February 2004 ( 2004) DIFFERENCES IN SELF-REGULATION FOR ONLINE LEARNING BETWEEN FIRST- AND SECOND-GENERATION COLLEGE STUDENTS Peter E. Williams*, and Chan M. Hellman** ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: Self-regulation is generally accepted as an important construct in student success within environments that allow learner choice, such as online courses. The purpose of the current study was to investigate differences between first- and second-generation college students ability to self-regulate their online learning. An ANCOVA, with comfort level using the computer as a control, provided evidence that first-generation students report significantly lower levels of self-regulation for online learning than their second-generation counterparts. ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: KEY WORDS: first-generation; self-regulation; self-regulated; online; online learning; Webbased instruction. INTRODUCTION Self-regulation, a central concept in social cognitive theory, refers to an individual s use of three cognitive processes toward goal attainment: self-monitoring, self-judgment and self-reaction (Bandura, 1986). It is believed that self-monitoring is the most important of these processes (Zimmerman and Paulsen, 1995). Zimmerman (1998, 2000) suggests that the process of self-regulation is cyclical involving three stages. The first stage, forethought, occurs when learners prepare for performance; stage two, performance/volitional control, involves attention and action; and the final stage of self-reflection occurs when learners review their performance toward the goal, making adjustments to their strategies as necessary. From this theoretical perspective, those who are highly self-regulating tend to set proximal goals, monitor progress toward those goals, and employ new strategies as needed when progress is insufficient. Zimmerman (1998) notes that skillful self-regulators have the following characteristics: the ability to set a specific proximal goal; a learning goal orientation *Stewardship Learning, Claremore, OK. **University of Oklahoma. Address correspondence to: Peter Williams, P.O. Box 2882, Claremore, OK 74018. 71 0361-0365/04/0200-071/0 2004 Human Sciences Press, Inc.

72 WILLIAMS AND HELLMAN as opposed to a performance goal orientation; high self-efficacy; an intrinsic interest in the subject at hand; a focus on performance (not easily distracted); the tendency to use self-instruction and/or imagery; self-monitoring of processes; self-evaluation; the tendency to attribute negative outcomes to faulty learning methods or practice rather than to ability limitations while attributing positive outcomes to ability; and the ability to adapt to contextual factors. The Development of Academic Self-Regulation Schunk and Zimmerman (1996) suggest that social as well as self-directed activities contribute to self-regulation. Parents, teachers, and other significant adults form part of the social forces affecting the development of self-regulation. Schunk (1998) found modeling to be important in the development of selfregulated practice skills for mathematics. In a study of the relationship between family context variables and the development of self-regulation skills among college students, Strage (1998) found that students who reported parents as authoritative and family context as emotionally close were more likely to rate their self-regulation skills high. When parents were perceived as authoritarian and the family context as nagging or enmeshed, however, students reported concern for a lack of self-regulation skills. Steinberg (1996) showed that parents of highachieving students had high expectations regarding grades, and they monitored their children closely. Indeed, parent involvement and family context are important in the development of self-regulated learning in children. Zimmerman (1998) emphasizes that for students to develop self-regulation, learning tasks must include opportunities for them to practice these strategies on their own. He suggests homework is invaluable because it provides students with the practice necessary to routinize a study skill (p. 11). Self-regulation skills are acquired through social sources such as parents and teachers as well as through instructional activities. Within the academic environment, self-regulated learners set academic goals and monitor and react to their perceived progress toward these goals (Schunk, 1996). Schunk argues, students must regulate not only their actions but also their underlying achievement-related cognitions, beliefs, intentions and affects (p. 359). Academic self-regulation depends on students developing a confidence in their ability to perform well on learning tasks (e.g., academic self-efficacy; Bouffard-Bouchard, Parent, and Larivee, 1991; Schunk and Ertmer, 2000). The influence of self-regulation on academic success has been demonstrated repeatedly (Pintrich and De Groot, 1990; Pressley and Ghatala, 1990; Schunk, 1989; Williams and Hellman, 1998; Zimmerman, 1990; Zimmerman and Bandura, 1994). Self-regulating students tend to be more mentally active during instruction (Pintrich and Schrauben, 1992; Schunk, 1990). Hattie, Biggs, and Purdie s (1996) meta-analytic study with K 16 students revealed that learning

SELF-REGULATION FOR ONLINE LEARNING 73 outcomes improved as a result of learning skill instruction and that when multiple learning skills are targeted, learning outcomes are more likely to improve. Lan (1998) found that students enrolled in a statistics course who employed certain self-monitoring behaviors tended to score higher on course examinations. Finally, Williams and Hellman found that self-regulation was significantly correlated with grade point average among first-generation community college students. Academic success in these and other studies is measured in a variety of ways including grade point average (Williams and Hellman, 1998), performance on course examinations (Lan, 1998), and course completion (Zimmerman, 2002). Extending the research on self-regulation into the area of online learning makes sense, as more learner choice is allowed in online instruction; both Schunk (1996) and Zimmerman (1998) note that a learning environment that includes learner choice encourages self-regulation. Additionally, the U.S. General Accounting Office (U.S. GAO) reports that 1.5 million out of 19 million postsecondary students took a distance education course in the 1999 2000 school year (U.S. General Accounting Office [GAO], 2002). A few studies have investigated self-regulated learning among online learners with mixed results (Joo, Bong, and Choi, 2000; Land and Greene, 2000; McManus, 2000; Young, 1996). Young found that while differences in self-regulation did not play much of a role in instruction that is program controlled, the outcomes of high-level self-regulators were significantly higher in instructional environments that allowed learner control. Asynchronous online instruction allows a minimum of two choices that traditional instruction does not: where to study and when to study. This being the case, learner self-regulation can be more critical to academic success in online courses. It is critical to understand the relationship between self-regulation and the learning environment; moreover, it is important to program administrators and faculty to have an idea which students will exercise self-regulation skills. Statement of the Problem The importance of technological self-efficacy to learner success in online instruction is intuitively obvious and supported in the recent literature (Al- Khaldi and Al-Jabri, 1998; Hill and Hannafin, 1997; Joo et al., 2000). It is clear from the existing literature that academic self-regulation is an important skill predicting academic success. However, little evidence exists comparing firstand second-generation college students ability to self-regulate within an asynchronous learning environment. 1 Important differences between first- and second-generation students in traditional learning environments have been well documented. For example, Hellman and Harbeck (1996) found that first-generation college students possessed significantly lower levels of academic self-efficacy compared to their second-generation counterparts. First-generation college

74 WILLIAMS AND HELLMAN students tend to have lower levels of college persistence and often have lower academic performance levels (Blau and Duncan, 1967; London, 1989, 1992, 1996). Given these important distinctions and the recognized value of self-regulated learning to academic success, the purpose of the current exploratory study was to compare first- and second-generation college students ability to selfregulate their learning in an online educational environment. Based on the available literature, it was hypothesized that first-generation students enrolled in online courses would report lower scores on self-regulation behaviors germane to the online learning environment compared to their second-generation counterparts. METHOD Participants and Procedure Eight hundred and twenty-nine unduplicated college students enrolled in courses delivered online (via Internet) from a rural regional university located in the southern plains of the United States were the target population for the present study. Of these students, 14.1% reside in a different state than the institution. Additionally, 64.1% were female, 28.3% were characterized as minorities, and 61.0% were enrolled part time at the institution. Finally, 15.6% were coded as nondegree-seeking or undecided relative to an academic major. As part of the design of these online courses, students are periodically directed to a URL and asked to voluntarily participate in various assessment initiatives (e.g., course/instructor evaluations) in support of the institution s assessment plan. For the purposes of this study, a beginning of course online survey (Web-based) to assess entry-level student characteristics was designed to assist the institution in evaluating constructs that may contribute to student success in the online learning environment. More specifically, online students accessed the survey after they logged in to their course for the first-time. Online students were encouraged to participate on a voluntary basis and informed that their responses would remain confidential. The instrument was password protected with responses downloaded into an electronic database. This database was only accessible to the Director of Distance Education and Institutional Research staff. It was not available to any other faculty or staff member. Seven hundred and eight of these students completed the online survey representing a participation rate of 85.4%. Of these responding students, 63.6% were female and 40.7% indicated they were first-generation college students. The age of the participating students ranged from 18 to 61 years, with a mean of 29.64 years (SD = 8.91). Regarding educational objective, 80.8% reported their intention was to receive a degree from the institution, while 10.6% indicated their objective was to transfer to another institution. Finally, 52.0% reported this was their first online course from this institution.

SELF-REGULATION FOR ONLINE LEARNING 75 Measures For the purpose of the current study, researchers developed the dependent variable, self-regulation behaviors for online learning. A subscale of four items was developed based on the Multi-Dimensional Self-Efficacy Scales of Bandura (1989) and according to his guidelines (Bandura, 1997). Each item was presented with a five-item Likert-type response format with 1 being Not well at all to 5 being Very well. The composite mean for these items was 15.55 (SD = 2.84). A principal components analysis resulted in a single factor being extracted accounting for 61.79% of the variance. The eigenvalue was 2.47 with a scree plot supporting the extraction of a single factor. Table 1 presents the results of the item analysis for the four-item scale. As shown in Table 1, the corrected item to total score correlations are relatively strong ranging from.54 to.68. Indeed, removing any of the four items would result in a decreased internal consistency. The resulting Cronbach alpha of.79 suggests a reasonable level of reliability. Given the findings of the item analysis and the single component structure, the content homogeneity of the item scores suggests a reasonable measure of self-regulation for online learning. The independent variable, parent education level, was measured with two items asking the participants to report the highest education level attained for both their mother and father. Respondents were presented with a five-item forced choice response. These included: (a) Did not finish high school, (b) High school/ged, (c) Technical training, (d) Some college, and (e) College degree. As mentioned previously, 40.7% reported that neither parent had an educational attainment level of Some college or a College degree. TABLE 1. Item Analysis for the Self-Regulation for OnLine Learning Scale Corrected Item to Total Alpha if Item Mean SD Correlation Deleted 1. How well can you overcome computer and technology related problems? 4.06 0.85.64.73 2. How well can you use electronic library resources to get information for class assignments? 3.92 0.97.68.70 3. How well can you remember information you have read online or in textbooks for exams and projects? 3.81 0.86.54.77 4. How well can you participate in online class discussions? 3.76 0.92.56.77 Note: Cronbach s alpha =.79.

76 WILLIAMS AND HELLMAN Because online courses require extensive use of a personal computer and the ability to navigate the Internet, a covariate was included in the design. Specifically, a single item (M = 4.43; SD = 0.82) asked the students to report their comfort level using a computer. The response format was a 5-point, Likert-type format ranging from (1) Not at all to (5) Very comfortable. Keppel (1991) argues that in order for the use of a covariate to be efficient it should have at least a moderately strong relationship to the dependent variable. Within the results of this study, the correlation between self-regulated behaviors for online learning and comfort with using the computer was.579. RESULTS Prior to testing the hypothesis of this study, the statistical assumptions for the ANCOVA test were assessed (Stevens, 1996). Given the online learning environment, participants were tested individually, thus the assumption of independence is likely not violated. Additionally, skewness ranged from 0.39 to.64, with kurtosis ranging from.010 to 0.03 on each of the four-items of the dependent variable suggesting the assumption of normality was not violated. The assumption for homogeneity of variance was also not violated, Levene s F(1, 705) = 0.02; p =.886. The correlation between the covariate and the dependent variable was.579 and was similar for both first- and second-generation students (e.g., linearity and homogeneity of regression slopes). The covariate was measured using a single item. Thus, reliability for the covariate could not be assessed in the current study using a cross-sectional design. Nevertheless, the mean scores on the covariate for both first- and second-generation students did not differ significantly, F(1, 706) = 0.01; p =.917, suggesting differences between the adjusted means are not a function of measurement error (Stevens, 1996). Taken as a whole, it is argued that the statistical assumptions germane to the ANCOVA model were not violated. Table 2 presents the results of the ANCOVA used to test the hypothesis that controlling for comfort with using the computer, first-generation students would score significantly lower on self-regulated behaviors for online learning compared to their second-generation counterparts. The covariate accounts for significant variance, F = (1, 704) 355.01; p.001, thus making the model more powerful. Indeed, given the correlation between the covariate and dependent variable and that first- and second-generation students had similar mean scores on the covariate, the covariate substantially reduced the error variance. Furthermore, the ANCOVA results show that, controlling for the effects of comfort level using the computer, first-generation college students (M = 15.34; SD = 2.85) scored significantly lower, F = (1, 704) 4.13; p.05; partial η 2 =.006, compared to second-generation students (M = 15.69; SD = 2.83) on self-regulated behaviors for online learning. Although the effect size is small, the results of

SELF-REGULATION FOR ONLINE LEARNING 77 TABLE 2. Results of the ANCOVA for Self-Regulated Behaviors for On-Line Learning Comparing First- and Second-Generation Students Sum of Mean Partial Source Squares df Square F Ratio P Value Eta 2 Corrected Model 1926.47 2 963.24 179.47.000.338 Intercept 1031.27 1 1031.27 192.14.000.214 Comfort Level (Covariate) 1905.42 1 1905.42 355.01.000.335 Generation Level (Between) 22.17 1 22.17 4.13.043.006 Within 3778.50 704 5.37 Total 176664.00 707 Corrected Total 5704.967 706 Note: R 2 =.338 (Adjusted R 2 =.336). this study show some support for the hypothesized differences between firstand second-generation college students on self-regulating behaviors germane to the online learning environment. DISCUSSION The original hypothesis of this study, that first-generation students reported lower levels of self-regulation than second-generation students, was supported by the data. There is a growing body of evidence dealing with first- and secondgeneration learners and how they learn, but the authors did not locate any research dealing with the learning characteristics of these groups and how these characteristics interact with the online learning environment. Yet the growth of online learning in higher education and the additional choices that are characteristic of an online learning environment make this research crucial to the understanding of online learners. First-Generation Students Our findings are supported by previous research on first-generation college students, namely that they do not have the same skill level as second-generation students and therefore are less successful. First-generation college students performance has been shown to be lower than that of second-generation students in a variety of measures (Blau and Duncan, 1967; London, 1989, 1992, 1996; Piorkowski, 1983; Pratt and Skaggs, 1989; Riehl, 1994; Terenzini, et al., 1994; Terenzini, Springer, Yaeger, Pascarella, and Nora, 1996; York-Anderson and Bowman, 1991). First-generation students are less socially integrated, more likely to be older, to be married with dependents, and more likely to receive

78 WILLIAMS AND HELLMAN financial aid than their second-generation counterparts (Nunez and Cuccaro- Alamin, 1998). These characteristics may contribute to lower levels of persistence among first-generation students. Various explanations have been explored, including survivor guilt (Piorkowski, 1983), family support (York-Anderson & Bowman, 1991), and separation theory (London, 1989), or, as Terenzini et al. (1994) explain it, first-generation students were breaking, not continuing a family tradition (p. 63). This research confirms that many first-generation college students are taking online courses (40.7% of online students were first generation), at least at the regional state university included in the research. These first-generation students may lack the self-regulation skills needed to be successful learning online. This finding has several implications for practitioners and researchers. Implications These results reinforce the notion that orientation to online learning may be necessary, particularly for first-generation college students. Institutions that serve this population in online programs might consider including a strong orientation to online learning and coordinating with existing program structures that already serve first-generation students. Moreover, faculty and instructional designers might consider structuring courses in ways that promote self-regulation (McMahon, Cowan, and Oliver, 2001; Dabbagh and Kitsantas, 2002). Ley and Young (2001) analyzed the research on self-regulatory differences between high-achieving and low-achieving learners and extracted four principles for imbedding self-regulation skill development in instruction: Guide learners to prepare and structure an effective learning environment ; organize instruction and activities to facilitate cognitive and metacognitive processes ; use instructional goals and feedback to present student monitoring opportunities ; and provide learners with continuous evaluation information and occasions to self evaluate (pp. 94 95). Zimmerman (2002) found that seeking help was the self-regulatory behavior most likely to predict attrition and retention in Web-based courses. University administrators and faculty can create an environment that encourages help-seeking by providing multiple avenues through which students can seek help and by clearly communicating these venues to students. As the phenomenon of online learning grows and becomes part of the fabric of the higher education experience, the need to understand how learner characteristics interact with the learning environment grows accordingly. Although the effect size presented in this study is small, the findings suggest that first-generation students may benefit more than others from learning environments that encourage and support self-regulation. Moreover, providing more support for lower self-regulating learners does not appear to decrease achievement among

SELF-REGULATION FOR ONLINE LEARNING 79 higher self-regulating learners (Young, 1996). The continued study of learner characteristics and how they affect achievement in the online learning environment should guide interventions designed to improve instruction. Limitations While the findings presented in the current study present a possible new application of individual differences relative to online learning success, several limitations must be considered. For example, the sample represents students taking courses offered by a small regional university. Data are not available at the institution to determine if first-generation students enroll at a greater rate in online or in traditional courses. Students attracted to other universities offering asynchronous learning environments may produce conflicting results. Additionally, the cross-sectional nature of this study is not able to address the stability of self-perceptions that may be influenced by unmeasured sources. The operational definition of the covariate, comfort level using a computer, is likely not fully encompassed by the single item and clearly reliability scores cannot be computed in a cross-sectional design. Thus, future studies would be advised to include a more psychometrically sound measure to see if replication takes place. Another limitation of the current study is the potential for existing rival hypotheses (other than comfort level using a computer) to that of parental education level. In the current study, the mean age of participants (29.64 years) could raise the question of whether parents are the predominant referent group for these nontraditional age learners. It could easily be argued that the educational level of a spouse or sibling could have a stronger influence than the older students parents. This study is exploratory in its application of existing theories to a new learning environment and for this reason needs replication. Nevertheless, given these limitations, the current findings are promising relative to understanding academic success in the online learning environment. ENDNOTE 1. The term second-generation college student is used in this report to refer to all students having at least one parent who attended a postsecondary institution. Other labels such as later, nonfirst, or continuing generation might be more semantically accurate, but second-generation is common in the literature, more conceptually pleasing, and therefore preferred by the authors. REFERENCES Al-Khaldi, M. A., and Al-Jabri, I. M. (1998). The relationship of attitudes to computer utilization: New evidence from a developing nation. Computers in Human Behavior 14(1): 23 42.

80 WILLIAMS AND HELLMAN Bandura, A. (1986). Social Foundations of Thought and Action, Prentice-Hall, Englewood Cliffs, NJ. Bandura, A. (1989). The multidimensional self-efficacy scales. Unpublished test, Stanford University, Stanford, CA. Bandura, A. (1997). Self-Efficacy: The Exercise of Control, W. H. Freeman, New York. Blau, P., and Duncan, O. D. (1967). The American Occupational Structure, John Wiley, New York. Bouffard-Bouchard, T., Parent, S., and Larivee, S. (1991). Influence of self-efficacy on self-regulation and performance among junior and senior high-school age students. International Journal of Behavioral Development 14(2): 153 164. Dabbagh, N., and Kitsantas, A. (2002). Supporting self-regulation in student-centered web-based learning environments. World Conference on E-Learning in Corp., Govt., Health, & Higher Ed. 2002(1): 1366 1369. Hattie, J. A., Biggs, J., and Purdie, N. (1996). Effects of learning skills intervention on student learning: A meta-analysis. Review of Research in Education 66(2): 99 136. Hellman, C. M., and Harbeck, D. J. (1996). Academic self-efficacy: Highlighting the first-generation student. Journal of Applied Research in the Community College 4(2): 69 75. Hill, J. R., and Hannafin, M. J. (1997). Cognitive strategies and learning from the World Wide Web. Educational Technology Research and Development 45(4): 37 64. Joo, Y. J., Bong, M., and Choi, H. J. (2000). Self-efficacy for self-regulated learning, academic self-efficacy, and Internet self-efficacy in web-based instruction. Educational Technology Research & Development 48(2): 5 17. Keppel, G. (1991). Design and Analysis: A Researcher s Handbook (3rd Ed.), Prentice Hall, Upper Saddle River, NJ. Lan, W. Y. (1998). Teaching self-monitoring skills in statistics. In: Schunk, D. H., and Zimmerman, B. J. (eds.), Self-Regulated Learning: From Teaching to Self-Reflective Practice, Guilford Press, New York, pp. 86 105. Land, S. M., and Greene, B. A. (2000). Project based learning with the World Wide Web: A qualitative study of resource integration. Educational Technology Research & Development 48(1): 45 68. Ley, K., and Young, D. B. (2001). Instructional principles for self-regulation. Educational Technology Research and Development 49(2): 93 103. London, H. B. (1989). Breaking away: A study of first-generation college students and their families. American Journal of Education 97(2): 144 170. London, H. B. (1992). Transformations: Cultural challenges faced by first-generation students. In: Zwerling, L. S., and London, H. B. (eds.), First-Generation Students: Confronting the Cultural Issues, New Directions for Community Colleges 80(Winter), Jossey-Bass, San Francisco, pp. 5 11. London, H. B. (1996). How college affects first-generation students. About Campus 1(5): 9 13, 23. McMahon, M., Cowan, E., and Oliver, R. (2001). Promoting self-regulated learning in an on-line environment. World Conference on Educational Multimedia, Hypermedia and Telecommunications 2001(1): 1299 1305. McManus, T. F. (2000). Individualizing instruction in a web based hypermedia learning environment: Nonlinearity, advance organizers, and self-regulated learners. Journal of Interactive Learning Research 11(2): 219 251. Nunez, A. M., and Cuccaro-Alamin, S. (1998). First-generation college students: Undergraduates whose parents never enrolled in postsecondary education. Report No. NCES 98-082, National Center for Educational Statistics, Washington, DC.

SELF-REGULATION FOR ONLINE LEARNING 81 Pintrich, P. R., and De Groot, E. V. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology 82(1): 33 40. Pintrich, P. R., and Schrauben, B. (1992). Students motivational beliefs and their cognitive engagement in classroom academic tasks. In: Schunk, D. H., and Meece, J. L. (eds.), Student Perceptions in the Classroom, Erlbaum, Hillsdale, NJ, pp. 149 183. Piorkowski, G. K. (1983). Survivor guilt in the university setting. The Personnel and Guidance Journal 61(10): 620 621. Pratt, P. A., and Skaggs, C. T. (1989). First generation college students: Are they at greater risk of attrition than their peers? Research in Rural Education 6(2): 31 34. Pressley, M., and Ghatala, E. S. (1990). Self-regulated learning: monitoring learning from text. Educational Psychologist 25(1): 19 33. Riehl, R. J. (1994). The academic preparation, aspirations, and first-year performance of first-generation students. College and University 70(1): 14 19. Schunk, D. H. (1989). Self-efficacy and cognitive skill learning. In: Ames, C., and Ames, R. (eds.), Research on Motivation in Education. Vol. 3. Goals and Cognitions, Academic Press, San Diego, pp. 13 44. Schunk, D. H. (1990). Goal setting and self-efficacy during self-regulated learning. Educational Psychologist 25(1): 71 86. Schunk, D. H. (1996). Learning Theories: An Educational Perspective (2nd Ed.), Prentice Hall, Englewood Cliffs, NJ. Schunk, D. H. (1998). Teaching elementary students to self-regulate practice of mathematical skills with modeling. In: Schunk, D. H., and Zimmerman, B. J. (eds.), Self- Regulated Learning: From Teaching to Self-Reflective Practice, Guilford Press, New York, pp. 137 159. Schunk, D. H., and Ertmer, P. A. (2000). Self-regulation and academic learning: Selfefficacy enhancing interventions. In: Boekaerts, M., Pintrich, P. R., and Zeidner, M. (eds.), Handbook of Self-Regulation, Academic Press, San Diego, pp. 631 649. Schunk, D. H., and Zimmerman, B. J. (1996). Modeling and self-efficacy influences on children s development of self-regulation. In: Juvonen, J., and Wentzel, K. R. (eds.), Social Motivation: Understanding Children s School Adjustment, Cambridge University Press, New York, pp. 154 180. Steinberg, L. (1996). Beyond the Classroom, Simon & Schuster, New York. Stevens, J. (1996). Applied Multivariate Statistics for the Social Sciences (3rd Ed.), Lawrence Erlbaum Associates, Mahwah, NJ. Strage, A. (1998). Family context variables and the development of self-regulation in college students. Adolescence 33(129): 17 33. Terenzini, P. T., Rendon, L. I., Upcraft, M. L., Millar, S. B., Allison, K. W., Gregg, P. L., and Jalomo, R. (1994). The transition to college: Diverse students, diverse stories. Research in Higher Education 35: 57 73. Terenzini, P. T., Springer, L., Yaeger, P. M., Pascarella, E. T., and Nora, A. (1996). Firstgeneration college students: Characteristics, experiences, and cognitive development. Research in Higher Education 37: 1 22. United States General Accounting Office (GAO; 2002). Distance Education: Growth in distance education programs and implications for federal education policy. Statement of Cornelia M. Ashby, Director of Education, Workforce, and Income Security Issues, GAO-02-1125T [Online]. Available: http://www.gao.gov/new.items/d021125t.pdf Williams, J. E., and Hellman, C. M. (1998). Investigating self-regulated learning among first-generation community college students. Journal of Applied Research in the Community College 5(2): 83 87.

82 WILLIAMS AND HELLMAN York-Anderson, D. C., and Bowman, S. L. (1991). Assessing the college knowledge of first generation and second-generation college students. Journal of College Student Development 32(2): 116 122. Young, J. D. (1996). The effect of self-regulated learning strategies on performance in learner controlled computer-based instruction. Educational Technology, Research and Development 44(2): 17 27. Zimmerman, B. J. (1990). Self-regulating academic learning and achievement: The emergence of a social cognitive perspective. Educational Psychology Review 2(2): 173 201. Zimmerman, B. J. (1998). Developing Self-Fulfilling Cycles of Academic Regulation: An analysis of Exemplary Instructional Models. In: Schunk, D. H., and Zimmerman, B. J. (eds.), Self-Regulated Learning: From Teaching to Self-Reflective Practice, Guilford Press, New York, pp. 1 19. Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In: Boekaerts, M., Pintrich, P. R., and Zeidner, M. (eds.), Handbook of Self-Regulation, Academic Press, San Diego, pp. 13 39. Zimmerman, B. J., and Bandura, A. (1994). Impact of self-regulatory influences on writing course attainment. American Educational Research Journal 31(4): 845 862. Zimmerman, B. J., and Paulsen, A. S. (1995). Self-monitoring during collegiate studying: An invaluable tool for academic self-regulation. In: Pintrich, P. (ed.), New Directions in College Teaching and Learning: Understanding Self-Regulated Learning (No. 63, Fall), Jossey-Bass, San Francisco, pp. 13 27. Zimmerman, M. C. (2002). Academic Self-Regulation Explains Persistence and Attrition in Web-Based Courses: A Grounded Theory. Doctoral dissertation, Northern Arizona University, Flagstaff. Received December 23, 2002.