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  1. Doctor of Management in Community College Policy and Administration
  2. Recommended For You
  3. Conference for Community College Advancement | CASE
  4. ISBN 13: 9780415881272

Edward P.

Philo A. Marybeth Gasman. Noah D. Penny A.

Doctor of Management in Community College Policy and Administration

Terrell L. John S.

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New Releases. Understanding Community Colleges. Description Understanding Community Colleges provides a comprehensive review of the community college landscape-management and governance, finance, student demographics and development, teaching and learning, policy, faculty, and workforce development-and bridges the gap between research and practice.

Item differences between time points were determined by calculating the normalized difference for each item across the entire sample from the beginning of the introductory series to the advanced time point, according to the formula.

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This formula accounts for initial item difficulty by calculating the proportion of the available difference achieved at the later time point. This work was approved under protocols at Arizona State University , , University of Colorado—Boulder , University of Maine—Orono , University of Nebraska—Lincoln , University of Washington—Seattle , and all piloting institutions. We determined the extent to which these alignments could explain variation in student responses.

We generated Rasch models to determine the extent to which student responses to individual items were consistent with their broader performance on the test. We analyzed person reliability as a metric for the consistency of student responses across all the items on a test. We first developed a model in which all the items were considered as a single scale, which produced an acceptable reliability of 0. We also analyzed each core concept and subdiscipline as separate models and found that the reliabilities for these models were variable, ranging from 0.

These lower reliabilities likely stemmed from the comparatively smaller number of items in each subcategory and suggest that individual student scores for core concepts and subdisciplines should be interpreted with caution. However, these scores may still be useful when aggregated at the cohort level for identifying broader performance trends. Rasch point measures represent the correlations point-biserial coefficient between item responses and modeled student ability scores Linacre, b.

Conference for Community College Advancement | CASE

The vast majority out of of the items had positive values, whereas only three items 15b, 36d, and 45d had negative point measures, indicating that higher-performing students did slightly worse than their lower-performing counterparts. We elected to leave these three items on the instrument, because they were interpreted appropriately during student interviews, they tested important concepts, their low correlations could be explained by poor student performance, they did not hinder the overall instrument from achieving acceptable reliability levels, and they had negligible effects on total scores.

We analyzed Rasch outfit mean-square statistics as a metric for the degree to which responses to each item fit the test model. For the outfit mean-square statistic, all of the items had acceptable fits based on having values between 0. The Mantel-Haenszel test analyzes whether two groups show significant differences on individual items beyond what would be expected given the overall scores of these students Crocker and Algina, In both cases, one item was easier for the nonreference group, and the other item was harder for the nonreference group.

We elected to leave these items on the instrument, because they showed no explicit signs of bias during student interviews, they seemingly had no distinguishing features that related to the particular demographic variable, and they had a neutral net effect on overall scores. Rasch modeling estimates person and item parameters based on how students answer each item. This is particularly useful for instruments such as GenBio-MAPS that use a test administration design in which students only answer a subset of all the questions, because student ability scores account for the difficulty of the particular items answered by each student.

However, we also recognize that many institutions might lack the necessary expertise, software, and sample size to analyze test data using item response models. Thus, we compared Rasch analyses with classical student and item metrics to determine whether there were functional differences between these two analytic approaches. Given that most institutions using GenBio-MAPS will employ classical test statistics and that these metrics correlate very closely with Rasch-based measures, the remaining analyses will use classical test results.

We next sought to understand broad student performance patterns based on overall test scores. We generated a linear mixed-effects model to control for sampling variance and estimate the contributions that various factors make to overall scores Table 3. We found that administration time point had a large impact on student scores, modeled as a difference of 6.

By comparison, class standing i. Self-reported grade point average GPA had an effect of roughly 3. In comparison with their reference group, we found a positive effect for students who took AP Biology in high school 2. In each of these separate models, we found no significant effect for the interaction term, indicating that the discrepancies seen for each demographic variable remain consistent across the major and do not narrow or widen at later time points.

Student raw score distributions at the different time points based on A overall scores, B core concept scores, and C subdiscipline scores. Central bars represent median overall percent correct, boxes represent inner quartiles, and whiskers represent 5th and 95th percentiles. Linear mixed-effects model on the effect of student demographic characteristics on overall percent correct.

Estimates for the other nominal variables indicate the modeled effect based on being a member of the italicized focal group in comparison with the indicated reference ref group. Although we did not design the GenBio-MAPS instrument for the purpose of comparing institutions, we tested whether it has the important property of detecting institution-specific outcomes. This term was significant, indicating that institutions show different trajectories across the time points Supplemental Material 8. We further plotted average raw overall student performance for the 11 institutions with data at all three points Figure 4.

These institutions showed a range of different profiles across the three time points. The patterns did not necessarily reflect different classes of institutions based on the Carnegie basic classification , as each pattern could be observed for different institution types. In some cases, students at an institution had equivalent increases in performance between consecutive time points, suggesting continual gains across the curriculum.

ISBN 13: 9780415881272

In other cases, students at an institution showed little difference between the first two time points, but a larger increase between the later time points or, conversely, a large difference between the first two time points followed by a smaller difference across the later time points. In these cases, a plateau between adjacent time points could highlight a time period with little growth and periods for programs to consider potential improvements. Student performance at different institutions across time points. Points represent average raw overall percent correct at the beginning of the introductory series, end of the introductory series, or advanced time points.

While overall scores can detect broader patterns in student performance, programs also need higher-resolution information to identify areas for growth. Thus, we began by plotting core concept and subdiscipline scores at the different time points Figure 3, B and C. In addition to subcategory scores, institutions can further examine performance at the item level to pinpoint specific areas of proficiency and deficiency.

The 10 items showing the highest differences had normalized differences above 0.

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  • We also identified the 10 items for which students demonstrated the lowest differences Table 5. These items could show low differences because they were either challenging at both time points or relatively easy at both time points. For most of the items, the initial percent correct started and remained low i. The items spanned all five core concepts and covered a more even range of biological scales. In articulating the core concepts, Vision and Change created a conceptual framework for departments to place at the center of their undergraduate curricula.

    However, despite these important advances, the biology education community has lacked mechanisms to directly measure whether general biology programs are successfully teaching the core concepts.