This essay has been submitted by a student. This is not an example of the work written by our professional essay writers.
Community College Students
Today, approximately 64% of all community college students entering community college are not qualified for college level work. Moreover, in order for California to continue to compete economically there needs to be a dramatic increase in the percent of students earning associates and/or bachelor’s degree. In order to help increase student success, a student success model centered on evidence-based decision making is proposed.
The student success model uses research to inform decisions. The informed decisions lead to either professional development activities and/or interventions, which ultimately leads to student success. The model also includes a continuous evaluation process of the intervention. The student success model can be employed individually by researchers and colleges; however, in order for the change to occur system wide the community college system needs to change policy, culture, and the current funding structure.
There are approximately 71 million people attending schools in the United States (National Center for Education Statistics, NCES, 2003). In California the community college system serves over 2.5 million students each year and is one of the largest educational systems in the world (California Community College Chancellor’s Office, CCCCO, 2005). One of the great challenges of the 21st Century is how to educate the large number of community college students who are under-prepared. For instance, only 36% of the students enrolling in community colleges are qualified for college level work (Hoachlander, Sikora, Horn, & Carroll, 2003). In addition, community college students are more likely to be from low-income families and to be under-prepared for college level work than in past generations (Shulock, Moore, Offenstein, & Kirlin, 2008).
Currently, the need for an educated workforce is great and the California Community College System is falling behind other states (Shulock et al., 2008). For instance, in order for California to compete economically California needs to increase the percent of students earning an associates or bachelor’s degree every year by over 50%. Consequently, in order to achieve better outcomes there needs to be a change in policy (Shulock et al.). Therefore, the purpose of this paper is to propose a community college student success model to guide future decisions to help increase student success of community college students. The model is intended to serve as a filter by which decisions are made.
An ecological model allows for the examination of the challenges faced by community colleges today and to account for the multiple influences on student success Prilleltensky & Nelson, 2000). For example, an ecological model allows for the examination of the interactions between student attitudes, institutional interventions, policy, and how each of these facilitates student success (Little & Kantor, 2002).
As an illustration, students may need a financial incentive to perform better, institutions may be able to increase student success by developing more restrictive learning pathways, and policy could be changed to allow more flexibility in the programs that are offered to students (Shulock et al., 2008). Finally, an important component of the student success model proposed here is that prevention is better than allowing students to get in a place where they are overwhelmed and feel unsuccessful (Caughy, O’Campo, & Brodsky, 1999; Prilleltensky & Nelson, 2000; Tolen, Guerra, & Kendell, 1995).
Students. Community colleges serve a diverse group of students (Bailey, Jenkins, & Leinbach, 2005; Bailey & Morest, 2006). For instance, community college students are more likely to be Hispanic, Native American, or African American, and are also more likely to need financial support. Equally important, community colleges have a long history of providing access to higher education for many students who otherwise would not have been able to attend college.
As an illustration, in fall 2006, many of California Community College (CCC) students were from traditionally under-represented groups (i.e. African American, Native American, or Hispanic; California Community Colleges Chancellor’s Office, CCCCO, 2006b). In addition, many CCCs have a majority of students from traditionally under-represented groups. Therefore, in order for community college students to be successful we need to identify a wide range of engagement strategies (Community College Survey of Student Engagement [CCSSE], 2006; Feldman et al., 2004).
Student attitudes play an extremely important role in student success and impact any intervention to increase success. For instance, gender research indicates that male students who receive a low amount of external rewards from their parents to achieve academically as children were more likely to have higher academic goals (Davis, Winsler, & Middleton, 2003). On the other hand, females who received similar amounts of external rewards were more likely to be intrinsically motivated, which is more likely to lead to successful outcomes.
Next, when looking at age, community college students who are 55 years old or older are more likely to rate their mathematical skills lower than younger students (Laanan, 2003). Finally, traditionally under represented groups are more likely to enroll in developmental courses (Bailey et al., 2005).
Community colleges are expanding the roles of institutional research offices by asking for research that can be used in the decision making process (Morest & Jenkins, 2007). Moving institutional research offices from the traditional role of accountability reporting to one of improving student success through evidenced-based research has many challenges. As an illustration, the Community College Research Center randomly sampled 189 college administrators to help identify these challenges (Morest & Jenkins).
The results indicated that most institutional research offices do not have the staffing to move beyond traditional accountability reporting, the data that would be used in the evidence-based research needs to be cleaned and standardized across departments, and the faculty and administrators at many of these institutions are skeptical about the accuracy of the results because of the advanced nature of the research techniques that are being used.
Policy. A significant challenge to any evidence-based decision making model in California will most likely involve the Title V Education Code (Shulock et al., 2008). Namely, evidence may suggest that mandatory placement is extremely effective at improving course grades and increasing the likelihood that students will persist from one semester to the next. Mandatory placement means that students would be required to complete their English, reading, and math courses prior to enrolling in college level courses (e.g.: sociology, history, economics, etc.).
The Title V Education Code (California Office of Administrative Law, COAL, 2008) states the following in relation to prerequisites and mandatory placement: “Prerequisites establishing communication or computational skill requirements may not be established across the entire curriculum unless established on a course-by-course basis.” Due to this, the only method of requiring students to complete their English, reading, and math prior to enrolling in college level courses would involve conducting a prerequisite validation study on almost every college level course offered at every community college in California (Shulock et al., 2008).
Traditionally, the main purpose of community colleges was to provide access to college for students who would not otherwise be able to attend an institution of higher education (Bailey & Morest, 2006). Today the focus is not only access, but now also includes academic achievement. For instance, Federal and State governments are expecting colleges to show that students are achieving academically (Morest & Jenkins, 2007). As a result, the proposed student success model presented here is centered on evidence-based decision making (Shulock et al., 2008; Caughy et al., 1999; Tolan et al., 1995). In addition, an example of how the model is expected to work is presented as well.
First, in order to develop interventions research in the form of literature reviews and research conducted by institutional research offices is used to inform discussions in relation to what will help students succeed Next, these discussions lead to learning interventions directly (e.g.: learning communities, mandatory placement, etc.) as well as indirectly through professional development activities. Finally, the learning interventions are evaluated to identify whether or not they improve student success and if they need to be adjusted for certain student groups with different backgrounds (Caughy et al., 1999; Tolan et al., 1995).
Illustration of a Learning Pathway based on the Student Success Model
The following uses the student success model to illustrate a concrete action that could be undertaken to help increase the rate at which community college students are successful. Evidence for employing the learning pathway suggested here is discussed in later sections of this manuscript. First, students would begin their affiliation with a community college by taking a battery of tests and diagnostics.
Currently, students already take assessment tests that assess their level of preparation for English, math, and reading. In addition to these, students could also be required to take an assessment like the Multiple Learning Strategies Questionnaire (MLSQ). The MSLQ quantifies six different aspects of motivation and eight different types of learning strategies (Garcia-Duncan & McKeachie, 2005).
Research studies could be conducted to identify which support strategies (e.g.: learning communities, supplemental instruction, etc.) worked best with different groups of students (Caughy et al., 1999; Tolan et al., 1995). Next, after completing the assessments, students would meet with a counselor and an education plan would be developed that is based on the results. Finally, it would be mandatory for students to complete pre-collegiate level math, English, and reading courses prior to taking a college level course.
Funding would be a substantial challenge to the student success model proposed here. As an illustration, any college deciding to enact mandatory placement would need to dramatically increase the number of offerings of pre-collegiate math, English, and reading courses, as well as increase the number of counseling staff, and obtain assessment software and licensing for the diagnostic portion of the plan.
Specifically, if only 36% of students enrolling in community colleges are qualified for college work (Hoachlander et al., 2003), then 64% of the sections being offered need to be in pre-collegiate math, English, and/or reading. Moreover, pre-collegiate courses (i.e. math, English, and reading) only receive approximately 60% of the funding that colleges receive for credit courses (Shulock et al., 2008; Center for Student Success, Research and Planning Group for the California Community Colleges [CSS, RP], 2007). Finally, most research offices do not have the staffing and resources to engage in research to help inform the decision making process (Morest & Jenkins, 2007).
Theoretical Basis for the Suggested Learning Pathway
First, the recommended learning pathway proposed here is based on a model of informed decision making. Dewey (1895/1964) strongly believed that psychologist should provide educators with the techniques that research has indicated improve student success.
For instance, Dewey described a good educator as one who was aware of how a student’s environment can impact learning, is a student of teaching methods that work, and is aware of how certain teaching methods may work in one subject but not in another. Moreover, Dewey also felt that teachers need to employ psychological research methods in order to determine whether new teaching methods improve student success.
Next, the learning pathway discussed above is also routed in theory and supported with research. As an illustration, one of the most effective teaching methods is to create associations with what is being taught to what students are interested in (Hall, 1965; Thordndike, 1913; Dewey, 1895/1964). Equally important, Tinto (1997) also states that in order to increase student involvement in their education, educators need to connect a student’s academic experiences with their social experiences.
For example, research has indicated that learning communities increases student success as well as help to create associations for students (Hotchkiss, Moore, & Melinda, 2005; Johnstone, 2005; Tinto, Goddsell-Love, & Russo, 1994). In addition, teaching techniques like supplemental instruction (Peled & Kim, 1996), service learning (Johnstone, 2005; Michael, 2005; Tinto, 1997), and collaborative learning (Lundberg, 2003), to name a few, help to both engage students and increase student success.
Finally, student motivation to learn is also an important component leading to student success (Quintilian, 2001; Locke, 1959). For instance, Quintilian thought that the best way to learn was to make learning fun because it helped to motivate students. Equally important, Locke discussed motivation in terms of negative associations. Consequently, focusing on motivation is important because motivation is related to persistence, which is extremely important in higher education (Graham & Weiner, 1996).
Role of Psychologists, Social Scientists, and Researchers
Psychologists and/or researchers play an extremely important role in the evidence-based decision making model (Morest & Jenkins, 2007). First, institutional researchers need to not only conduct research that identifies effective student success strategies, we also need to train administrators and faculty how to ask questions and interpret results.
Next, institutional researchers also need to effectively translate sophisticated analyses into something that can be understood by decision-makers (Morest & Jenkins). Finally, institutional researchers need to actively seek social change (O’neill, 2004; Marsella, 1998; Prilleltensky, 1997, 1989) to help increase the rates at which students are successful by finding opportunities to provide and conduct evidence-based research.
There are many challenges that would need to be overcome with this proposed plan. Two challenges that come to mind initially are that, in all likelihood, California’s Title V Education Code would need to be changed. On the other hand, as discussed previously, numerous prerequisite validation studies on almost every course could be conducted instead of attempting to change Title Education Code. Conducting the research might be a more plausible course of action. Finally, in order for real change to occur there needs to be a change in culture, funding structure, and policy (Shulock et al., 2008; CSS, RP, 2007; Morest & Jenkins, 2007).
Obviously, a positive potential outcome of employing the student success model is increased numbers of students who are successful (Shulock et al., 2008; Morest & Jenkins, 2007). Specifically, a change of this magnitude could lead to students achieving their goals earlier in their life, more college graduates, and more students earning Bachelor and Graduate degrees (Shulock et al.). On the other hand, a potential unintended consequence might be disproportionate impact. Disproportionate impact occurs when a group has a higher percentage of students receiving a recommendation or a requirement than in the percentage of persons in the population. Consequently, students from a particular racial, ethnic, gender, age, or disability group might be required to take more pre-collegiate courses than another group.
There are two main ethical concerns with employing an evidence-based decision making model to improve student success. The first is disproportionate impact. The other is one of access. The Title V Education Code has guidelines in §55201(e)(2)(B) for when a group is disproportionately impacted that could be employed with this model. The Education Code states that the District needs to develop a plan for correcting the disproportionate impact. In addition, a differential prediction study could be employed to determine whether the predictive power of the recommendation is weaker or stronger for a particular group and whether the prediction over or under predicts performance of a particular group (Young & Kobrin, 2001).
Evaluating the Impact of the Student Success Model
An important aspect of the student success model proposed here involves evaluating the outcomes and providing that information to decision makers on a regular basis so that they can adapt programs to what research indicates is working. Consequently, the evaluation of whether a program and/or intervention leads to student success needs to occur on a regular basis.
According to Morest and Jenkins (2007), evidence-based research requires the use of higher level statistics and methodologies like time-series analyses, multinomial logistic regression, and path analysis. The most important aspect of the evaluation component is that it needs to continually inform decision-makers—administrators and faculty—about whether or not the program is improving student success. Hence, there is a continuous loop of research informing practice.
On the whole, in order to effect change and dramatically increase student success the California Community College System needs to change how colleges are funded as well as support a culture of evidence-based decision making. Employing an evidence-based student success model system will require changes in policy, culture, and funding structure (Shulock et al., 2008). At the same time, institutional researchers can begin to move the culture of the college they are working at by finding opportunities and decision-makers at their individual campuses who are open to using evidence to make decisions.
Bailey, T.R., Jenkins, D., & Leinbach, D.T. (2005). Community College Low-Income and Minority Student Completion Study: Descriptive Statistics from the 1992 High School Cohort. New York: Community College Research Center, Teachers College, Columbia University.
Bailey, T. & Morest, V.S. (2006). The community college equity agenda in the twenty-first century. In T. Bailey and V.S. M (Eds.) Defending the Community College Equity Agenda (pp. 246-270). Baltimore, MD: Johns Hopkins University Press.
California Community Colleges Chancellor’s Office (CCCCO). (March, 2005). Retrieved March 26, 2008 from http://www.cccco.edu/SystemOffice/News/PressReleases/CollegesNationalUnivSignTransferAgreements/tabid/1113/Default.aspx
California Office of Administrative Law (COAL, 2008). Retrieved May 17, 2008 from http://government.westlaw.com/linkedslice/default.asp?Action=TOC&RS=GVT1.0&VR=2.0&SP=CCR-1000
Capriccioso, R. (2006). Community college conundrum. Inside Higher Ed. Retrieved March 26, 2008 from http://www.insidehighered.com/news/2006/04/14/calcc
Caughy, M.O., O’Campo, P., & Brodsky, A.E. (1999). Neighborhoods, families, and children: Implications for policy and practice. Journal of Community Psychology, 27, 615-633.
Center for Student Success, Research and Planning Group for California Community Colleges (CSS, RP). (2007). Basic Skills as a Foundation for Student Success in California Community Colleges. Retrieved May 17, 2008 from http://www.cccbsi.org/Websites/basicskills/Images/Lit_Review_Student_Success.pdf
Davis, K.D., Winsler, A., & Middleton, M. (2006). Students’ perceptions of rewards for academic performance by parents and teachers: Relations with achievement and motivation in college. Journal of Genetic Psychology, 167, 211-220. Retrieved August 11, 2007 from the Academic Search Premier database.
Dewey, J. (1964). What psychology can do for the teacher. In R.D. Archambault (Ed.), John Dewey on education: selected writings (pp. 195-211). Chicago, IL: University of Chicago Press. (Original work published 1895)
Garcia-Duncan, T., & McKeachie, W.J. (2005). The making of the motivated strategies for learning questionnaire. Educational Psychologist, 40, 117-128. Retrieved March 26, 2008 from the PsycINFO database.
Graham, S. and Weiner, B. (1996). Theories and principles of motivation. In D.C. Berliner and R.C. Calfee (Eds.), Handbook of Educational Psychology (pp. 63-84). New York: Prentice Hall.
Hall, G.S. (1965). Child-study and its relation to education. In C.E. Strickland and C. Burgess (Ed.) Health, growth, and heredity: G. Stanley Hall on natural education (pp. 74-90). New York: Teachers College Press. (Original work published 1900)
Hotchkiss, J.L., Moore, R.E., & Melinda, P.M. (2005). Freshman learning communities, college performance, and retention. Working Paper Series (Federal Reserve Bank of Atlanta), 22, 1-26. Retrieved April 14, 2006 from the Academic Search Premier database.
Johnstone, Robert (2005). Community college pre-collegiate research across California: Findings implications, and the future. iJournal, 9. Retrieved March 3rd, 2005 from http://www.ijournal.us/issue_09/ij_issue09_05_CraigHayward_01.html
Laanan, F.S. (2003). Older adults in community colleges: Choices, attitudes, and goals. Educational Gerontology, 29, 757-776. Retrieved August 11, 2007 from the PsycINFO database.
Little, L., & Kantor, G. K. (2002). Using ecological theory to understand intimate partner violence and child maltreatment. Journal of Community Health Nursing, 19, 133-145.
Locke, J. (1959). Book II: Chapter 33: Of the association of ideas. In A.C. Fraser (Ed.), An Essay Concerning Human Understanding: Vol. 1 (pp. 527-535). New York: Dover Publications. (Original work published 1701)
Lundberg, C.A. (2003). Nontraditional college students and the role of collaborative learning as a tool for science mastery. School Science and Mathematics, 103, 8-18.
Marsella, A.J. (1998). Toward a ‘global-community psychology:’ Meeting the needs of a changing world. American Psychologist, 53, 1282-1291.
Michael, R.L. (2005) Service-learning improves college performance. Academic Exchange Quarterly, 9, 110-115. Retrieved April 15, 2006 from the Expanded Academic ASAP database.
Morest, V.S. & Jenkins, D. (2007). Institutional research and the culture of evidence at community colleges. Community College Research Center. Retrieved August 4, 2007 from http://ccrc.tc.columbia.edu/Publication.asp?UID=515
O’neill, P. (2004). The ethics of problem definition. Canadian Psychology, 46, 13-20.
Peled, O.N. & Kim, A.C. (1996). Evaluation of supplemental instruction at the college level. (ERIC Document Reproduction Service No. ED410777)
Prilleltensky, I., & Nelson, G. (2000). Promoting child and family wellness: Priorities for psychological and social interventions. Journal of Community and Applied Social Psychology, 10, 85-105.
Prilleltensky, I. (1997). Values, assumptions, and practices: Assessing the moral implications of psychological discourse and action. American Psychologist, 52, 517-535.
Prilleltensky, I. (1989). Psychology and the status quo. American Psychologist, 44, 795-802.
Quintilian. (2001). Book one: Chapter 1: Elementary education. In D.A. Russell (Ed.), Quintilian, The Orator’s Education: Books 1-2 (pp. 65-83). London: Harvard University Press. (Original work published AD 1)
Shulock, N. & Moore, C. (2007). Rules of the game: How state policy creates barriers to degree completion and impedes student success in the California community colleges. Institute for Higher Education Leadership & Policy. Retrieved March 26, 2008 from http://www.csus.edu/ihe/PDFs/Rules%20of%20the%20Game%20FINAL.pdf
Shulock, N., Moore, C., Offenstein, J., & Kirlin, M. (2008). It could happen: Unleashing the potential of California’s community colleges to help students succeed and California thrive. Institute for Higher Education Leadership & Policy. Retrieved May 2, 2008 from http://www.csus.edu/ihe/PDFs/R_ItCouldHappen_02-08.pdf
Thorndike, E.L. (1913). Educational Psychology Vol. 1: The Original Nature of Man. New York: Teachers College, Columbia University.
Tinto, V. (1997). Classrooms as Communities: Exploring the Educational Character of Student Persistence. The Journal of Higher Education, 68, 599-623.
Tinto, V., Goodsell-Love, A., & Russo, P. (1994). Building learning communities for new college students: A summary of research finding of the collaborative learning project. University Park, PA: Pennsylvania State University, National Center on Postsecondary Teaching, Learning, and Assessment.
Tolan, P. H., Guerra, N. G., & Kendall, P. C. (1995). ). A developmental-ecological perspective on anti-social behavior in children and adolescents: Toward a unified risk and intervention framework. Journal of Consulting and Clinical Psychology, 63, 579-584.
Young, J., & Kobrin, J. (2001). Differential validity, differential prediction, and college admission testing: A comprehensive review and analysis. College Entrance Examination Board, New York. Retrieved May 17, 2008 from http://www.collegeboard.com/research/pdf/differential_validity_10539.pdf