Session: AtD – Use of Data Mary M. Cauley

MARTIN COMMUNITY COLLEGEACHIEVING THE DREAMCOMMUNITY COLLEGES COUNTIIPS ConferenceCharlotte, North CarolinaJuly 24-26, 2006 Session: AtD – Use of Data Mary M. Cauley

AtD Colleges must use data relative to student success to plan and evaluate interventions. • Martin Community College (MCC) used student engagement data in plans to increase student success.

MCC is a small, rural, single-campus institution with fewer than 1,000 students.

Research shows that the more actively engaged students are - - ־ with faculty and staff ־ with other students ־ with the subject matter they study - - the more likely they are to learn and to stay in college until they achieve their academic goals.

Student engagement is a valuable yardstick for assessing whether and to what extent an institution’s educational practices are likely to produce successful results. The term “successful results” is defined as more students across all groups learning at higher levels and achieving their academic goals.

MCC wanted to know: ־ if our students are actively engaged in the institution, and if so, - at what level they are engaged. • To do this, we asked our students about their level of engagement. • We listened to student voices through the administration of the Community College Survey of Student Engagement (CCSSE)

A growing body of research has identified institutional practices and student behaviors that promote student engagement. • The CCSSE survey targets these areas for improvement.

Good practice in undergraduate education: • Encourages student-faculty contact. • Develops reciprocity and cooperation among students. • Encourages active learning. • Gives prompt feedback. • Emphasizes time on task. • Communicates high expectations. • Respects diverse talents and ways of knowing.

CCSSE measures students’ levels of engagement in good educational practices and what they gain from their college experience. • The good practices in undergraduate education are used as benchmarks. • Benchmarks are groups of conceptually related items that address key areas of student engagement, learning, and persistence.

CCSSE’s 5 benchmarks denote areas that educational research has shown to be important in quality educational practice. • Benchmarks of effective educational practice: 1. Active and collaborative learning. 2. Student effort. 3. Academic challenge. 4. Student-faculty interaction. 5. Support for learners.

Benchmark No. 1Active and Collaborative Learning • 4a. Asked questions in class or contributed to class discussions. • 4b. Made a class presentation. • 4f. Worked with other students on projects during class. • 4g. Worked with classmates outside of class to prepare class assignments. • 4h. Tutored or taught other students • 4i. Participated in a community based project as a part of a regular course. • 4r. Discussed ideas from your readings or classes with others outside of class (students, family members, co-workers, etc.)

These benchmarks are tools that can be used to compare college performance across benchmarks, to similarly-sized institutions, and to the full CCSSE population of community colleges.

Part-time vs. full-time student enrollment status: • Research samples are based on classes not students. • CCSSE samples entire classes of students. • Therefore, more full-time students are selected for the surveys, simply because the full-time students are enrolled in more classes than part-time students. Thus, the full-time students are more likely to be selected. • While 2/3 of the students attending participating community colleges attend part time, the survey showed the opposite.

CCSSE assigns weights to responses based on respondents’ enrollment status, thereby producing more accurate measures of student engagement. • When aggregate data are reported for both part-time and full-time students, it is weighted by enrollment status. • Goal: To increase community college student engagement and ultimately completion rates. • We can use survey results to improve our programs and services for students.

Five ways you can analyze and interpret your CCSSE results: (Consider the results in terms of your own college mission, institutional focus, and student characteristics.) 1. Compare your college to the national average. (Look at areas in which you are most different. Then identify strengths and areas for improvement.) 2. Compare your college to “high performing colleges.”

3. Measure your college’s performance in terms of your least-engaged student group. You can use your raw data file to look at • first-generation students • developmental students • high-risk students (single parents, having to care for dependents attending college part time, low income, etc.) • You can also compare engagement levels of • minority groups • returning female students • other groups

4. Gauge work in areas most strongly valued. Examine results in light of your institution’s vision, mission, and/or strategic objectives. Which of the benchmarks are most important to your college? 5. Compare where you are now with where you want to be. Perform a gap analysis—where you are now versus where you want to be. What issues have been identified for your college through self-study for reaffirmation of accreditation? Or recommendations from the visiting team?

Report to the faculty and to the college community on actions taken in response to the survey results. • Involve your students in discussions about your CCSSE survey results. The survey emphasizes that students have a role to play in their own learning, and these are their observation on their educational experiences. Encourage student-faculty discussions.

Both positive and negative survey data can help improve educational practice and performance. Use the positive data to showcase your college’s best practices.

Sources of Research • 1984 – National Institute of Education • 1987 – “Seven Principles for Good Practice in Undergraduate Education. Chickering and Gamson • 1991- How College Affects Students. Pascarella and Teranzini • 1993 – Leaving College. Tinto Thank you

Session: AtD – Use of Data Mary M. Cauley