Download Course-Taking Patterns of Community College Students Beginning

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Course-Taking Patterns of Community College Students Beginning in STEM: Using Data Mining
Techniques to Reveal Viable STEM Transfer Pathways
Abstract
This research examines course-taking patterns of beginning community college students enrolled in one
or more STEM courses during their first year of college, and how these patterns are mapped against
upward transfer in STEM fields of study. Drawing upon postsecondary transcript data, collected as part of
the Beginning Postsecondary Students Longitudinal Study (BPS:04/09), this study employs data mining
techniques that, although underutilized in higher education research, are powerful and appropriate
analytical tools for investigating complex transcript data. Thus, focusing on a pivotal yet extremely
understudied topic dealing with postsecondary STEM education and pathways, this study offers new
insight into course and program features that contribute to efficient and effective academic STEM
pathways for interested community college students.
Purpose of the Study
In recent years, researchers and policymakers have grappled with how to address the shortage of
college students pursuing science, technology, engineering, and mathematics (STEM) degrees (Fox,
2003; Hagedorn & DuBray, 2010; Hagedorn & Purnamasari, 2012; Lowell & Salzman, 2007). While the
demand continues to rise as the United States looks to maintain a global competitive edge, both
academically and economically, there exists a serious deficit in the number of students entering STEM
areas of study and successfully completing college degrees in these fields (Dowd, 2012; Espinosa, 2011).
In order to address this problem, numerous policies, programs, and initiatives have focused on improving
STEM participation, persistence, and completion at postsecondary institutions.
The role of community colleges in this important endeavor cannot be overemphasized. Nationally,
nearly 1,200 community colleges enroll over 8 million students annually, including 46% of all
undergraduates (American Association of Community Colleges, 2015). Community colleges also serve a
disproportionately large percentage of minority students (Cohen, Brawer, & Kisker, 2014), who are
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underrepresented in STEM fields but embody a vital talent pool for the country’s STEM future.
Traditionally underappreciated, these public 2-year institutions have the potential to increase the number
and diversity of students pursuing STEM degrees (Hagedorn & Purnamasari, 2012). With their transfer
function, community colleges also have the capacity to assist students to continue their education at 4year colleges and universities, and succeed in a STEM baccalaureate.
In recent years, the pivotal role of community colleges in STEM education has gained national
attention. In 2011, the National Academy of Sciences, the National Science Foundation, and the Carnegie
Institution for Science co-sponsored the Summit on Community Colleges in the Evolving STEM
Education Landscape, which highlighted the vital role these public 2-year institutions can play in
expanding the nation’s educational pathways for students pursuing STEM degrees and occupations. With
this policy priority in mind, it is critical to identify viable STEM educational pathways facilitated by
community colleges.
Although a fair amount of empirical work has been devoted to STEM education, much of this
research has focused on high schools and 4-year institutions (e.g., ACT, 2006; Crisp, Nora, & Taggart,
2009; Porter & Umbach, 2006; Wao, Lee, & Borman, 2010), rather than the role of community colleges
and how they may assist students to transfer into and succeed in STEM areas of study at 4-year
institutions. In particular, we know virtually nothing about what course-taking trajectories are followed by
community college students who are interested in studying STEM fields. Nor do we have much
information on which course-taking pathways align with successful transfer to 4-year schools. Despite the
policy relevance of transfer in STEM, this issue simply has not garnered a sufficient body of robust
evidence that can illuminate unique course and program mechanisms underlying effective STEM transfer
pathways (Dowd, 2012). This void in the literature warrants new and robust empirical efforts, as coursetaking patterns are a prime indicator of the academic experience and progression of community college
students who are largely commuters (Hagedorn & Kress, 2008).
To tackle the gap in the STEM transfer literature, this study examines the course-taking trajectories of
beginning community college students, and the resulting transfer outcomes as related to STEM.
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Specifically, this study asks: What course-taking patterns are most contributive to upward transfer in
STEM fields? By exploring and identifying patterns of course-taking that are common to those students
who successfully transfer in STEM versus other transfer outcomes, this study can shed light on course
pathways STEM-aspiring students navigate through community colleges, and which particular trajectories
are most viable for STEM transfer. This knowledge will assist educators and policymakers in creating and
facilitating effective and efficient STEM transfer pathways for interested community college students.
Specifically, institutions can use results from this research to improve their curriculum and program
offerings, and strengthen intercollegiate course and program articulations to develop viable STEM
pathways.
Relevant Literature
A small but growing body of research has touched upon community college transfer into STEM fields
at 4-year institutions. A handful of studies have focused on the required academic preparation. Hagedorn
and DuBray (2010) highlighted the appropriate amount of preparation from community colleges as a
critical factor in successful STEM transfer, given that many students attending community colleges often
underperform in essential STEM courses such as math and science (Bragg, 2012). The courses needed to
perform at the level required for transfer into STEM fields often become gatekeeper courses, discouraging
many students from pursing this route (Hagedorn & DuBray, 2010; Packard, 2012). Therefore, scholars
have called for community colleges to improve both math and science courses and students’ progress
through them to promote STEM transfer and attainment (Hagedorn & Purnamasari, 2012; Hoffman,
Starobin, Laanan, & Rivera, 2010). Yet, there is little agreement on what constitutes “necessary” and
“appropriate” course preparation to achieve this goal. Furthermore, we have little empirical knowledge as
to which course-taking patterns help students progress in an efficient manner and adequately meet the
rigor of STEM course requirements.
Looking at the STEM transfer issue more holistically, involving both 2-year and 4-year institutions,
Bensimon and Dowd (2012) argued that course and program alignment and articulation between
community colleges and 4-year institutions can increase STEM transfer rates. However, there remains
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poor collaboration between community colleges and universities, as well as significant issues of curricular
distrust (Dowd, 2012; Gabbard et al., 2006). Clearly, additional research exploring factors associated with
curricular alignment and articulation is critical, especially in STEM fields. Otherwise, this problem will
continue to impede community college students on their educational journey toward baccalaureate
attainment in STEM fields. One promising approach would be to explore STEM-aspiring community
college students’ course-taking patterns and their connection to successful transfer. This knowledge will
inform both 2-year and 4-year institutions in their efforts to set up curricular and articulation agreements,
in order to facilitate STEM transfer and eventual completion.
Several other studies focused on education interventions, such as outreach programs, to improve
STEM transfer (e.g., Bensimon & Dowd, 2012; Packard, 2012). For example, outreach has been
suggested as a means to improve transfer by informing students of options to pursue a STEM degree
(Packard, 2012). Measures like this, however, have not pointed to how actual course-based pathways
influence students’ transfer in STEM, and no concrete or consistent results exist on a larger scale to
inform curricular improvement and change (Dowd, 2012; Packard, 2012).
In summary, a limited number of studies have explored various characteristics that help or hinder
community college students in their transfer access to STEM programs at 4-year institutions. Yet, these
studies invariably neglect a key dynamic—students’ actual course-taking pathways along the STEM
pipeline, and what course patterns are conducive to STEM transfer. Should this knowledge gap continue,
the empirical research base on STEM transfer would offer only limited insight into potential opportunities
for designing and structuring viable academic offerings and pathways. By increasing our knowledge on
course-taking patterns, we can effectively inform a number of current curricular concerns surrounding
STEM transfer, such as lack of articulation of coursework, lengthy remedial course sequences, and the
separation of special programs from the core curriculum (Dowd, 2012).
Conceptual Grounding of the Research
In order to develop informed and efficient interventions to improve student outcomes, this study
draws upon Bahr’s (2013) deconstructive framework that calls for an in-depth understanding of how
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students persist or fail to progress through community colleges. Bahr breaks down the actual process by
which students advance in their coursework toward any given end, whether it is an actual qualification,
transfer, subject competency, or other intended educational goal. As Bahr forcefully argued, without
understanding how students actually progress through their college programs, “institutional adjustments
and interventions will be more a product of guesswork than of sound and empirically-based reasoning” (p.
13). This emphasis on deconstructing student coursework progress necessarily calls for a robust analysis
of transcript data. Given that community colleges are largely commuter schools, many community college
students primarily engage with their college through coursework. Thus, exposure to courses represents the
primary college environment, and course-taking behaviors and patterns are the primary indicators of
student engagement with this academic environment. Indeed, as Hagedorn and Kress (2008) have long
established, for many community college students, the only trace of their presence is found in the
transcripts. As a whole, a student’s transcript serves as a map of the curriculum—the principal college
environment traveled by students. When analyzed appropriately, course-taking patterns may offer
valuable insight into a student’s academic history and momentum through college, and illuminate patterns
that effectively engage academic resources. Building upon these lenses, this study examines course-taking
patterns conducive to transfer in a STEM context.
Methods
Data Source and Study Sample
This study draws upon data from the Beginning Postsecondary Students Longitudinal Study
(BPS:04/09) and Postsecondary Education Transcript Study (PETS:09). Following a nationally
representative, first-time postsecondary beginning cohort in 2003–2004, BPS:04/09 contains survey data
at three points in time: in respondents’ first year of college, and then again three and six years after they
started postsecondary education. Of critical importance to the proposed study, transcripts were collected
under PETS:09 from all 3,030 eligible postsecondary institutions attended by the BPS respondents over a
6-year period. Of the eligible institutions, 2,620 (87%) provided transcripts for a total of 16,960 students.
PETS:09 offers most of the measures for this inquiry. These include detailed transcript records at the
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student-, course-, term-, degree-, institution-, and transfer-level. The transcript data are invaluable in
studying course-taking patterns, credit transfer, and the links among course-taking, institutional transfer,
and baccalaureate persistence and attainment in STEM.
To appropriately account for student intent in pursuing STEM areas of study, the study sample was
restricted to beginning postsecondary students at community colleges who were enrolled in at least one
non-remedial STEM course during the first year of postsecondary attendance. The definition of STEM
courses was based on the Classification of Instructional Programs (CIP) codes, available for each course
record in the PETS:09 dataset.1 Of the BPS panel respondents, 5,550 began at a public 2-year institution
and, among these community college entrants, nearly 2,810 successfully completed at least one course in
STEM fields during the first year.
Data Preparation and Formulation
Due to the highly complex nature of the transcript data, this study involved a substantial amount of
data preparation. Specifically, I performed a series of data cleaning, re-coding, and discretizing using the
following steps:
The first step was to extract all postsecondary records of each individual student. One complication
here was that the PETS:09 data contain transferred course records, resulting in duplications due to
multiple submissions of the same course records by different institutions for the same student when
transferring courses. To resolve this problem, I removed duplicated course entries to achieve unique
course records, i.e., one record per course. In the case of repeated courses, only the most recent record
was retained. Additionally, among the sampled transcript data, 27 course records were without course ID,
11 were designated as post-baccalaureate courses, 592 records bore zero credit, 46 were taken at lessthan-2-year institutions, and 57 did not have CIP codes for identifying their subject matter. These course
records were neither relevant nor contributive to the purposes of the study, and were therefore removed
from subsequent analyses.
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For a detailed description of STEM course classification, see the section on data preparation and formulation.
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Next, in order to better organize and describe students’ major field of study and course subject matter,
initial majors and course CIP’s (6-digit) were each recoded to generate two more data fields and
populated against their corresponding higher-level CIP categories: 2-digit and 4-digit CIP codes. The 2digit CIP represents the broadest level of CIP categorization and the 6-digit values are at the most detailed
level of categorization.
Normalized course grades (MTNGRAD, a common scale comparable across institutions) were
recoded to generate a field to indicate whether students completed the course or not, since course records
would only be meaningful if the student passed the course. If the normalized course grade was greater
than or equal to 2.0 (or a “passed” indicator is marked), this course record was retained in the analyses.
This set of data preparation and recoding procedures greatly assisted in making sense of the highly
unstructured transcript data in PETS:09. After these steps, the analytical dataset contained a total of
51,370 pre-transfer course records for the 2,810 students in the sample.
Following these steps, course data were organized into categories based on course subjects.
Specifically, STEM courses included those with the following 2-digit CIP codes: 01 (Agriculture
sciences), 03 (Natural resources and conservation), 11 (Computer and information science), 14
(Engineering), 15 (Engineering technologies/technicians), 26 (Biological and biomedical sciences), 27
(Mathematics and statistics), 40 (Physical sciences), 41 (Science technologies/technicians), and 47
(Mechanic/repair technologies/technicians). Note that course credits earned in the CIP 15, 41, and 47
categories are less likely to be transferable to a four-year institution given their strong occupational
orientation; therefore, these categories were further classified as “likely terminal” STEM courses, and the
rest of the STEM categories were designated as “likely transferable” STEM courses. Specifically, course
classifications include the following areas: (a) “likely transferrable” STEM courses; (b) “likely terminal”
STEM courses; (c) mathematics—courses within CIP 27 category, except for those designated as
remedial math; (d) English—courses within CIP 23 category, except for those designated as remedial
English; (e) remedial courses; and (f) other. The main “outcome” measure indicates upward transfer
among STEM-aspiring students beginning at community colleges. This measure was coded into three
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possible outcome scenarios: (a) upward transfer in STEM, if, over the 6-year period, students’ transcript
data indicate transfer into a baccalaureate program in the STEM CIP codes as indicated above (i.e., CIP
01, 03, 11, 14, 15, 26, 27, 40, 41, and 472); (b) upward transfer into non-STEM fields; and (c) did not
transfer to a baccalaureate institution. See Table 1 for a complete variable list.
[Insert Table 1 about here.]
Data Analyses
I applied several data mining techniques to explore what specific course-taking patterns contribute to
upward transfer in STEM. Data mining refers to a family of exploratory analyses that extract implicit and
useful patterns and relationships from massive quantities of data, rather than testing pre-formulated
hypotheses (Han, Kamber, & Pei, 2011; Luan & Zhao, 2006). Data mining and traditional statistical
procedures both can perform association and prediction analysis, but for this study, data mining is more
appropriate because of the complex, seemingly unstructured nature of transcript data in PETS:09, which
holds tens of, or even up to a hundred, course records for each student over numerous academic terms. It
would be extremely challenging to apply traditional parametric analysis to make sense of the wealth of
such data. Instead, employing data mining techniques to tease out meaningful and frequent patterns or
strong links among the course records and student attributes, I was able to more accurately mirror the
nuances and complexities within students’ course-taking trajectories. I used a combination of the
following data mining techniques, each serving a unique purpose while lending complementary
contextual information to the results revealed by one another.
Apriori algorithm: Frequent pattern/association rule data mining. I first analyzed students’
course-taking patterns (both sequential and non-sequential) by applying the “frequent pattern/association
rule data mining” (Han et al., 2011). An association rule is a pattern indicating that an itemset A
occurrence implies that another itemset B also occurred, i.e., A⟹B (if A then B), or A(antecedent)
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Note that, while at the two-year level, course credits earned in the CIP 15, 41, and 47 categories may be more
“terminal” in nature, there exist a number of four-year STEM programs under these categories. A few examples of
these programs with a strong occupational focus include baccalaureate programs in manufacturing engineering
technology and mechanical engineering technology, electrical and computer engineering technology, and
aeronautical, automation, and automotive engineering technology.
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implies B(consequent). The occurrence count (or percentage) of itemset A among the universal set U is
called support of A, and the occurrence count (or percentage) of itemsets A and B occurring together is
referred to as support (A&B). A frequent pattern is an association rule whose occurrence count is greater
than or equal to a set of thresholds (i.e., the so-called minimum support and minimum confidence) set by
the researcher. Therefore, an association rule is usually reported as:
A⟹B (support = x %, confidence = y %),
where support (A⟹B) = support (A&B)
= Pr (A&B) i.e., probability of A and B occurred together, and
Confidence (A⟹B) = Pr(A&B) / Pr(A) = Pr(B|A)
In this study, finding frequent patterns or association rules is essentially a process of searching for and
counting frequency of all existing itemsets (including their subsets) from all of the available course-taking
patterns. Clearly, as the amount of course data increases, this searching process becomes time-consuming.
Thus, when performing association rule data mining, the basic and popular Apriori algorithm (Agrawal &
Srikant, 1994) is utilized to identify the frequent patterns. In short, the Apriori algorithm is a method to
improve efficiency in mining large datasets by applying the Apriori property, which states that if a given
itemset does not satisfy minimum support, then none of its subsets will satisfy minimum support.
Therefore, based on the Apriori property, if the course itemset does not satisfy the minimum support
criterion, there is no need to check all of its subsets’ frequency of occurrence. Through this property, the
search process for frequent course patterns is substantially shortened. In this study, due to the large
amount of different patterns, setting minimum support and minimum confidence at a pair of high initial
values was fruitless in search of frequent patterns; therefore, the minimum support was set to 10%, and
minimum confidence value was set to 30%. After the frequent pattern mining, lift score, a measure of
correlation in data mining, was used to examine correlations between the discovered antecedent A and
consequent B.
Lift = Pr(A&B)/(Pr(A)* Pr(B))
If A and B are independent to each other, the lift score would be equal to one, i.e., Pr(A&B)=Pr(A)*
Pr(B); otherwise, A and B are somewhat correlated. The greater the lift score, the greater the correlation.
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Decision List algorithm. When using the Apriori algorithm, the antecedent and consequent can only
be in a binary format. That is, they can only show whether a condition happened or not, e.g., whether a
student takes biology courses in a particular semester. While patterns revealed as such can provide insight
into sequence of course-taking, they do not offer information on the “dosage” of course-taking, e.g., how
much biology the said student in the previous example took during the given term. To delve into this
aspect of course-taking, I also mined the course records using the Decision List algorithm. While
Decision List mining shares some similarities with the Apriori algorithm in mining frequent patterns, such
as mining for distinct patterns of behavior or characteristics derived from the data and identifying
frequency pattern results using the “if-then” structure, it has the added advantage of combining binary,
categorical, or continuous predictor variables as the antecedent or consequent variables. For example, a
discovered decision rule would look like the following in the study context:
If 0 < math < 6 credits and STEM > 0 credit, then transfer to 4-year STEM is TRUE
(segment size=x, consequent size=y, probability=z%), where
segment size = count of the antecedent,
consequent size = count of target outcome,
probability = consequent size/segment size
Here, the rule describes that x number of students follow this course-taking pattern (i.e., 0 < math < 6
credits and STEM > 0 credit), and y of them transferred into a baccalaureate STEM major; thus the
probability of STEM transfer is (y/x)*100 = z. Or, we may interpret this decision rule as: if a student
successfully took some but less than six credits of math courses and at least one STEM course other than
math, then this student’s possibility of transferring into a baccalaureate STEM major will be z% among
those who have the similar course-taking pattern.
Exhaustive CHAID algorithm: Decision tree (or classification rules) data mining. After
performing the steps described above, I used the Decision Tree algorithm to perform multi-dimensional
data mining. Decision Tree data mining is suitable for handling higher-level dimensional data (Han et al.,
2011). In particular, student demographic data fields were joined to the course dataset to help examine the
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relationship between student characteristics and course-taking behavior. Accordingly, multi-dimensional
association rules mining were executed to examine whether course-taking pathways to STEM transfer
differ among racial and gender groups. Given the disproportionately high STEM attrition rates among
female students and members of underrepresented minority groups (e.g., Anderson & Kim, 2006; Burke
& Mattis, 2007; Chen & Weko, 2009; de Cohen & Deterding, 2009; Riegle-Crumb & King, 2010;
Seymour, 1995), this set of nuanced analyses will help pinpoint potential areas of intervention for these
students in regard to their course trajectories.
The specific algorithm used in decision tree mining is Chi-squared Automatic Interaction Detector
(CHAID), which repeatedly utilizes tests for statistical significance (such as F-test for continuous data or
chi-squared test for categorical data) to split data into a tree structure with branches of nodes, referred to
as the decision tree. Based on the scenarios of the STEM transfer outcome, data were classified into
groups (or child nodes) according to the most significant predictor variable. This data splitting process
was repeated for predictor variables until the decision tree is fully grown, where, within each child node,
there are no more significant factors that can classify node-level data into sub-nodes. Each path tracing
from the root node to a leaf node is a classification rule. Usually, decision tree mining involves two steps:
(1) the learning step—to derive classification rules from historical data; and (2) the classification step—to
predict data classes of new data. Since data class prediction is beyond the scope of this study, only the
learning step is utilized to derive the Decision Tree/classification rules.
I used Microsoft Access for relational database manipulation, aggregation, and transformation,
Microsoft Excel PivotTable tool to facilitate the data manipulation, filtering, and aggregation, and IBM
SPSS Modeler (formerly SPSS Clementine) for transcript data mining.
Limitations of the Study
There are a few limitations associated with the study’s sample, data, and methodological approaches.
To begin with, while the sample is nationally representative of beginning community college students
who are enrolled in at least one STEM course during their first year, they are spread across hundreds of
community colleges. Course content, requirements, and instructional approaches vary based on program
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and institutional contexts. These nuanced potential variations beyond the subject matter, credits, and
course sequence are difficult to account for in a national study. Sample size permitting, applying similar
analytic approaches to a single institutional context is a direction worth pursuing in the future. Second,
although the study differentiates community college STEM courses based on their potential
transferability, further disciplinary breakdowns within STEM fields at both the 2-year and 4-year levels
are not viable given the sub sample sizes within specific STEM disciplines and data available. In this
sense, while the results hold implications for STEM transfer pathways in a global sense, program-specific
recommendations for promoting transfer in a particular discipline are yet to be explored in future
research. Finally, the data mining techniques employed in the study serve the purpose of making sense of
large volumes of unstructured transcript records, but they do not reveal pathways that are causal in nature.
Notwithstanding the descriptive and predictive value of the data mining approach, additional empirical
efforts can aid in generating evidence better at drawing causal inferences.
Discussion of Results
Table 2 provides descriptive statistics of the study sample, and categorizes the data according to
students’ transfer outcomes. During the study’s 6-year observation window, after appropriate weighting,
roughly 3.7% of the STEM-aspiring students beginning at community colleges transferred into a 4-year
STEM major, 23.2% transferred into a 4-year non-STEM major, and 73.1% did not transfer to a 4-year
institution. Among STEM transfer students, 55.5% were male students and 44.5% were females. Relative
to their share in the total sample (28.1%), underrepresented minority students reported a lower percentage
of transfer outcomes in both STEM (18.6%) and non-STEM areas (24.8%). Similar disparities were
observed, indicating that students who were single-parents, first-generation, non-traditional age (i.e., age
24 and above), or with low high school GPAs were less likely to transfer upward into STEM or other
areas of study.
[Insert Table 2 about here.]
Descriptive Profiles of Course-Taking Among STEM-Aspiring Students
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Results from a number of descriptive analyses provide a rich account of course-taking trajectories
among the study sample. Table 3 provides an overall description of students’ course credit distributions
during the first year of enrollment, as well as during the 6-year time frame. These data are presented
based on transfer outcomes and arranged according to the six course subject areas.
[Insert Table 3 about here.]
As shown in Table 3, students who eventually transferred into 4-year STEM earned credits within
“likely transferable” STEM and math course categories at a higher rate than students with different
transfer outcomes, both during the first year and throughout the 6-year period. In addition, non-transfer
students were proportionately earning more credits from the “likely terminal” STEM course category.
This is not surprising, because most “likely terminal” STEM courses are oriented toward technician
training. Non-transfer students are typically those who attend community colleges with the primary goal
of attaining jobs, and are thus more drawn to technician training courses for the purposes of gaining
technical skills and earning certificates or diplomas in career and technical education fields (Cohen et al.,
2014).
Table 4 provides a much more detailed and nuanced look at the data from Table 3 that is further
limited to the course data within STEM programs. Based on Table 4, one thing that distinguishes STEM
transfer students from their counterparts is STEM transfer students’ more concentrated course-taking
within physical sciences (CIP 40). Specifically, CIP 40 includes subjects in physics, astronomy,
chemistry, and geology. These subjects are foundational to the undergraduate STEM curriculum at both
the 2- and 4-year level, and differ from some of the computer (CIP 11) and math (CIP 27) courses, such
as word processing, spreadsheet, web design/programming, algebra, calculus, and statistics, that may be
more occupationally oriented or applicable to other non-STEM fields, such as a business major.
[Insert Table 4 about here.]
In addition, the data in Table 4 also suggest that non-STEM transfer and non-transfer students heavily
took courses in the areas of computer sciences (CIP 11) and math (CIP 27). To drill down further into this
pattern, Table 5 provides a deeper level of data exploration and a finer view of these two subject areas
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(CIP 11 and 27 categories). Findings show that non-STEM transfer and non-transfer students’ coursetaking within these areas concentrate more on general math and computer data entry (e.g., word
processing) courses. In contrast, STEM transfer students accumulate more computer programming (CIP
11.02), networking and telecommunications (CIP 11.09), and other math and statistics (CIP 27.99)
courses.
[Insert Table 5 about here.]
Course-Taking Patterns Related to Transfer in STEM: Data Mining Results
Table 6 provides a selected list of frequent course-taking patterns based on the lift scores discovered
using the Apriori algorithm in data mining for frequent patterns. The higher the lift score, the more
relevant the discovered antecedent-consequent pattern is. In general, “antecedent” course-taking patterns
that result in transfer in STEM as a “consequent” involve a combination of “likely transferrable” STEM
courses and math courses in the earlier terms of students’ community college attendance. In particular, it
is intriguing to note that, among STEM transfer students, despite the inevitable math-learning path, math
course-taking during the very first term does not appear as a frequent course-taking pattern. Instead, the
most viable course-taking trajectories contributing to STEM transfer, by and large, feature a pattern that
first introduces “likely transferrable” STEM courses during the first term (i.e., to “get their feet wet”
first), followed by math exposure during the subsequent terms. It is also plausible that STEM transferaspiring students may be advised to, or find it helpful to, take math after their initial exposure to subject
matter courses in STEM. This finding might suggest that a robust pathway to transfer in STEM may be
well paved through initial “priming” by setting up the substantive, disciplinary context, followed by
course-taking in math. In this sense, STEM courses serve as the foundation that contextualizes math
learning. This establishes the eventual pathway, given that math is the language of science, and math
achievement is critical to longer-term STEM attainment (Tyson, Lee, Borman, & Hanson, 2007).
When examining the course-taking paths leading to transfer in non-STEM majors, math courses taken
during the first term is the most salient feature of all identified patterns. Coupling this finding with results
shown in Table 5 regarding non-STEM transfer students’ first-year math-taking, 68.5% of the credits
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were earned within the CIP 27.01 category, many of which are college math and algebra courses. This
could speak to the fact that college-level math is required of nearly all college students (Bragg, 2012;
Cohen et al., 2014; Cohen & Ignash, 1994). In addition, this pattern suggests the pivotal role of
completing math early in charting a transfer pathway (STEM or other programs). This finding is
noteworthy, given the fact that, in the community college context, students do not always follow the
prescribed course sequence in the curriculum (Bahr, 2013; Crosta, 2014; Hagedorn, 2005; Hagedorn &
Kress, 2008; Zeidenberg & Scott, 2011).
[Insert Table 6 about here.]
Table 7 provides a list of course-taking patterns discovered by data mining using the Decision List
algorithm. We can observe that, for those who transferred into a 4-year STEM major, their course-taking
patterns focused on “likely transferable” STEM and math. By contrast, students of other transfer
outcomes took fewer STEM courses and more non-STEM or terminal STEM courses. These results align
with those revealed in Table 6, but further illustrate the “dosage” of optimal course-taking. For example,
completing more than four or five credits of “likely transferrable” STEM courses during the first term,
followed by more than three credits of math during the subsequent term, is a course pattern highly
contributive to STEM transfer. As revealed by the rules in Table 7, especially the first three rules with
relatively high probabilities, it is the coupling of a fair amount of transferrable STEM courses and math
that contributes to STEM transfer. In particular, these rules seem to imply that, in order to transfer in
STEM, a student should successfully complete at least four likely transferrable STEM credits; beyond
that, the greater the “dosage” of STEM courses, the more likely that students transfer in STEM, especially
when we combine multiple rules (e.g., 1.4 and 1.5) in Table 7 to interpret those most contributive coursetaking patterns. To accompany “likely transferable” STEM courses, three to four credits in math seem
optimal. However, one should note that this finding is based on a term-by-term observation and does not
imply the total amount of completed credits that is desirable. In order to engage the perspective on coursetaking in terms of total course credits, the decision tree diagram paints a more holistic picture.
[Insert Table 7 about here.]
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Figure 1 displays the patterns revealed by data mining using Decision Trees. Due to the extremely
large tree structure that is not feasible to display in this paper, selected results are presented here focusing
on the right-hand side of the full-grown tree structure, since the rules discovered in the left-hand side of
the tree structure have lower probabilities of transfer to four-year STEM. As suggested in Figure 1, taking
“likely transferable” STEM courses appears to be the most important factor of affecting students transfer
outcomes in STEM. At the root level (Node 1), the probability of transfer in STEM is 4.4%. If students
earned STEM credits greater than 23.25, their probability of transfer in STEM increases to 18.0% (Node
5). This aligns with the previous postulation that the greater the “dosage” of STEM courses, the more
likely students transfer into STEM. Students who earned less “likely transferable” STEM credits, but took
more math (Node 14 &15) or math with a good amount of STEM credits (between 11.99 and 23.25)
(Node 33, & 34) would also increase their probability of transfer in STEM.
With regard to demographic factors, assuming the same course-taking trajectories, male students have
higher probabilities of transfer in STEM compared to their female counterparts (Nodes 31 vs. 32).
Similarly, underrepresented minority (URM) students have lower probabilities of transfer in STEM
(Nodes 35 vs. 36) than students from majority groups.
These disparities based on gender and race reflect persistent problems surrounding women and
minority students’ inequitable access to STEM programs of study (Author, 2013; Chen & Weko, 2009;
Huang, Taddese, & Walter, 2000; Kienzl & Trent, 2009; Sax, Shapiro, & Eagan, 2011; Shapiro & Sax,
2011). While it is important to take a nuanced approach to unpacking course-taking trajectories to reveal
successful pathways, as Bahr (2013) noted, this behavioral perspective must be considered in light of
student backgrounds. Compelling evidence dovetails with this study’s finding, showing that, while
minority students may major in STEM at similar rates as their White and Asian American counterparts or
even persist earlier in the process, they seem to struggle later and drop out of STEM disproportionately
(Anderson & Kim, 2006; Chen & Weko, 2009). In addition, there is severe underrepresentation of women
in STEM fields (de Cohen & Deterding, 2009; Riegle-Crumb & King, 2010), largely due to the hostility
female students encounter from faculty and their male peers, resulting in a loss of confidence in their
17
STEM abilities (Seymour, 1995). In light of this disparity, the study’s findings may suggest that, while
female and minority students, driven by their initial interest and plans, may follow the same course-taking
pathways as their male counterpart, these pathways may become leaky due to other confounding,
environmental factors that pull them out of STEM fields.
Implications and Conclusions
Several important implications emerge from this study’s findings. First, based on the course-taking
patterns that proved to be most salient along the STEM transfer pathway, curricular and programmatic
design that aims to facilitate transfer in STEM must feature a coherent, well-scaffolded sequence
combining transferrable STEM and math courses. Such design may benefit from emphasizing completing
such courses early while carefully charting their sequence, so that students can build their math skills
upon a foundational understanding of the substantive fields first. In addition, given the fact that
articulation of courses in STEM transfer remains a thorny issue (Chaplot, Rassen, Jenkins, & Johnstone,
2013; Jenkins & Cho, 2014; Roksa & Keith, 2008; Scott-Clayton, 2011), a more systematic and
consistent effort needs to occur to better streamline credit transfer and articulation agreements based on
the best course patterns discovered in order to help and promote student transfer, in STEM or other
programs. In this process, the transferability of the designated courses must be clearly communicated to
students in order to convince the course pattern followers to go on through the pipeline. Ambiguity
around transferability of courses may cause students to skip contributive courses, thus reducing the rate of
transfer.
Second, the persistent gender and racial gap in access to STEM programs of study warrants continued
research and policy interventions. In light of the findings from the study, we know that, even when
following the same early course-taking pathways, female and underrepresented minority students report
low probabilities in STEM transfer. We must continue the research to further explore how troublesome
problems within STEM classrooms and programs, such as stereotypes and lack of self-efficacy based on
gender and race, may be resolved. In addition, empirical efforts need to be further devoted to
understanding the nuanced and complex processes developing interest and grit in pursuing an upward
18
transfer pathway in STEM fields of study among female and minority students. To date, research
addressing women and minority students in STEM is predominantly situated within the 4-year college
context; more studies intentionally designed to zero in on the 2- to 4-year transfer access in STEM studies
for these students will hold vast promise to both reveal nuanced empirical findings and inform policy
efforts, in a purposeful way, that advance education opportunity for these traditionally underserved
students in STEM disciplines.
Third, it is critical to further involve analysis of transcript data, as transcripts form the map of a
student’s engagement with the community college (Hagedorn, 2005; Hagedorn & Kress, 2008). When
analyzed appropriately, transcript analysis offers valuable insight into a student’s academic history,
momentum through college, academic resources, and whether those resources are used wisely.
Community colleges may also use data mining techniques to discover the best contributive course
patterns in upward transfer for their students. Based on such analyses, articulating and designing course
packages and sequences according to the discovered patterns will help promote student transfer success.
In general, using transcripts as a data source and resorting to data mining techniques to analyze such data
will be essential in broadening our understanding of student enrollment and transfer patterns (e.g. Bach et
al., 2000; Hagedorn, 2005; Hagedorn, Cabrera, & Prather, 2010; Zeidenberg & Scott, 2011).
Despite the importance of understanding the connection between course-taking patterns and transfer
outcomes in STEM, remarkably little empirical knowledge exists to illuminate viable STEM pathways for
these students. Drawing upon rich and recent postsecondary transcript data, this research uses data mining
techniques that, although underutilized in higher education research, are powerful and appropriate
analytical tools to investigate complex transcript data. Thus, focusing on a pivotal yet extremely
understudied topic dealing with postsecondary STEM education, this study offers new insight into course
and program features that help contribute to efficient and effective STEM transfer pathways for interested
community college students. This knowledge can also assist educators and policy makers in improving
curriculum and program offerings and strengthening intercollegiate course and program articulations.
Such collaborative efforts will help cultivate social and organizational capital that helps institutions, both
19
2-year and 4-year, promote the long-term success of students (Amey, Eddy, & Campbell, 2010), thus
facilitating effective and efficient STEM educational pathways for interested community college students.
20
Acknowledgement: Blinded
21
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27
Table 1
List of Variables
Variable name
Description
BPS/PETS label
Sample selection criteria
RQ1&2: Sample restricted to respondents aspiring to earn at least a bachelor’s degree
and majoring in STEM fields upon entering a public 2-year institution
First institution type 2003–04
FSECTOR9
Major when first enrolled in 2003–04 is in STEM
fields (comparable to 2006, 2009)
MAJ04A
Highest degree ever expected in 2003–04 is a
baccalaureate or above
HIGHLVEX
RQ3: Sample restricted to beginning community college students who transferred into a
STEM major at a 4-year institution
Transcript: Level and control transfer type
QGTRTYPE
Transcript: Major at destination school
QGMAJRS
Outcome variables (against which course patterns are to be mapped)
STEM transfer
Transcript: Level and control transfer type
Transcript: Major at destination school
Baccalaureate attainment in
STEM by 2009
Being enrolled as a STEM major
at a 4-year institution by 2009
QGTRTYPE
QGMAJRS
Highest degree attained anywhere through 2009
ATHTY6Y
PETS reported field of bachelor’s degree
MT11BACH
Attainment or level of last institution enrolled
through 2009
PETS reported field of study in 2009
PRLVL5Y
MT11BACH
Transcript data used in data mining
Course attributes:
Course taker ID
Transcript : Student ID
ID
Course name
Course name
MTCRSNAM
Course ID
Course ID number
MTCRSID
Term ID
Course taken term ID
MTTMID
Term start date
Course taken term start date
MTTMBEG
Term end date
Institution where course was
taken
MTTMEND
Course 6-digit CIP code
Course taken term end date
Coded from institution’s IPEDS ID where course
was taken
Transcript institution’s IPEDS ID (or receiving
institution of transfer course)
PETS code (i.e., CIP) for course
Course top-level CIP category
Course top-level (2-digit) CIP code
MTPETGEN
Transcript institution
Credits counting toward GPA
Post baccalaureate course
Course credits count toward GPA indicator (1=yes,
0=no)
Post baccalaureate course attribute
MTINSTID
MTTRIPDS
MTPETC
MTCRDCT
MTCRSPBC
28
Variable name
Description
BPS/PETS label
indicator
Transfer course indicator
Course is a transfer course (1=yes, 0=no)
MTTRNSFR
STEM indicator
Course is in STEM category (1=yes, 0=no)
MTSTMFLG
Remedial course indicator
Course is remedial (1=yes, 0=no)
MTCRSREM
Normalized course grade
Normalized grade received for course
MTNGRAD
Normalized credit earned
Normalized credits received for course
MTNORMCR
Respondent’s gender
Dummy variable (1=female, 0=male)
GENDER
Respondent’s race/ethnicity
Race category
RACE
Respondent’s parental education
Whether respondent is a first-generation student,
recoded from parents’ highest level of education
(1=yes, 0=no)
PAREDUC
Demographic variables
29
Table 2
Demographics of the Sample by Transfer Outcomes
Transfer to 4-Year
TOTAL
Baccalaureate-Aspiring
Gender
Female
Male
Race/Ethnicity
URM
White & Asian
Income Group
1st Quarter
2nd Quarter
3rd Quarter
4th Quarter
Other Characteristics
Attended private high school
Being single parent
English as primary language
First-generation
Non-traditional age
Took Act or SAT
High School GPA Rank
1
2
3
4
5
6
7
Unknown
Transfer to 4-Year
Total
STEM
Non-STEM
Not Transferred
Weighted Weighted Weighted Weighted Weighted Weighted Weighted Weighted
N
%
N
%
N
%
N
%
31,890
3.7%
197,748
23.1%
625,222
73.1%
854,860
100.0%
29,143
91.4%
188,987
95.6%
470,272
75.2%
688,401
80.5%
14,178
17,711
44.5%
55.5%
119,183
78,565
60.3%
39.7%
355,761
269,461
56.9%
43.1%
489,123
365,738
57.2%
42.8%
5,926
25,964
18.6%
81.4%
48,995
148,753
24.8%
75.2%
185,596
439,626
29.7%
70.3%
240,517
614,343
28.1%
71.9%
6,818
13,291
8,272
3,508
21.4%
41.7%
25.9%
11.0%
48,905
50,691
53,888
44,264
24.7%
25.6%
27.3%
22.4%
142,131
168,355
163,961
150,775
22.7%
26.9%
26.2%
24.1%
197,854
232,337
226,121
198,548
23.1%
27.2%
26.5%
23.2%
2,416
210
25,456
9,590
1,938
24,802
7.6%
0.7%
79.8%
30.1%
6.1%
77.8%
12,312
6,189
173,500
56,971
16,636
154,685
6.2%
3.1%
87.7%
28.8%
8.4%
78.2%
31,439
69,634
565,903
278,527
202,214
282,971
5.0%
11.1%
90.5%
44.5%
32.3%
45.3%
46,167
76,033
764,859
345,088
220,788
462,458
5.4%
8.9%
89.5%
40.4%
25.8%
54.1%
562
859
1,754
5,274
8,594
9,102
5,745
1.8%
2.7%
5.5%
16.5%
26.9%
28.5%
18.0%
213
1,027
1,091
24,274
31,706
64,611
45,170
29,656
0.1%
0.5%
0.6%
12.3%
16.0%
32.7%
22.8%
15.0%
1,703
3,850
26,825
63,626
81,097
148,182
62,140
237,799
0.3%
0.6%
4.3%
10.2%
13.0%
23.7%
9.9%
38.0%
1,917
5,439
28,775
89,654
118,077
221,387
116,411
273,200
0.2%
0.6%
3.4%
10.5%
13.8%
25.9%
13.6%
32.0%
Note. Weight is the sampling weight WTD000 in BPS:04/09 dataset.
30
Table 3
Successful Pre-Transfer Credit Distribution by Transfer Outcomes and Course Categories
Success Course Type
First-year
STEM (transferable)
STEM (terminal)
Mathematics
English
Remedial
Others
Total
Six years
STEM (transferable)
STEM (terminal)
Mathematics
English
Remedial
Others
Total
Transfer to 4-Year
STEM
Credits
Earned
%
Transfer to 4-Year
Non-STEM
Credits
Earned
%
Not
Transferred
Credits
Earned
%
895
108
639
482
47
1,074
3,246
27.6%
3.3%
19.7%
14.9%
1.5%
33.1%
100.0%
2,603
125
2,878
3,296
289
8,987
18,179
14.3%
0.7%
15.8%
18.1%
1.6%
49.4%
100.0%
6,152
2,614
5,647
5,897
907
15,411
36,627
16.8%
7.1%
15.4%
16.1%
2.5%
42.1%
100.0%
6,786
637
2,022
1,043
61
5,045
15,595
43.5%
4.1%
13.0%
6.7%
0.4%
32.3%
100.0%
11,234
597
6,687
7,716
402
59,200
85,835
13.1%
0.7%
7.8%
9.0%
0.5%
69.0%
100.0%
17,977
6,261
10,179
10,426
1,299
52,880
99,022
18.2%
6.3%
10.3%
10.5%
1.3%
53.4%
100.0%
31
Table 4
Successful Pre-Transfer STEM Course-Taking and Credit Distribution by Transfer Outcomes
STEM Course Type
First-year
01 - Agriculture
03 - Natural resources
11 - Computer
14 - Engineering
15 - Engineering tech *
26 - Biological
27 - Mathematics
40 - Physical sciences
41 - Science tech *
47 - Mechanic *
Total
Six years
01 - Agriculture
03 - Natural resources
11 - Computer
14 - Engineering
15 - Engineering tech *
26 - Biological
27 - Mathematics
40 - Physical sciences
41 - Science tech *
47 - Mechanic *
Total
Transfer to 4-Year
STEM
Credits
Earned
%
85
3
158
47
93
237
639
365
15
1,642
490
201
853
1,156
615
1,830
2,022
2,256
22
9,445
Transfer to 4-Year
Non-STEM
Credits
Earned
%
Not
Transferred
Credits
Earned
%
5.2%
0.2%
9.6%
2.8%
5.7%
14.5%
38.9%
22.2%
0.0%
0.9%
100.0%
41
71
797
12
85
1,046
2,878
636
3
37
5,607
0.7%
1.3%
14.2%
0.2%
1.5%
18.6%
51.3%
11.3%
0.1%
0.7%
100.0%
384
88
2,760
117
878
2,063
5,647
739
10
1,727
14,413
2.7%
0.6%
19.2%
0.8%
6.1%
14.3%
39.2%
5.1%
0.1%
12.0%
100.0%
5.2%
2.1%
9.0%
12.2%
6.5%
19.4%
21.4%
23.9%
0.0%
0.2%
100.0%
179
440
2,470
181
438
4,562
6,687
3,402
44
116
18,518
1.0%
2.4%
13.3%
1.0%
2.4%
24.6%
36.1%
18.4%
0.2%
0.6%
100.0%
867
285
7,220
492
2,428
6,487
10,179
2,625
26
3,807
34,417
2.5%
0.8%
21.0%
1.4%
7.1%
18.8%
29.6%
7.6%
0.1%
11.1%
100.0%
Note. * denotes the STEM course categories that are likely to be non-transferable.
32
Table 5
Mathematics and Computer Science Course-Taking and Credit Distribution by Transfer Outcomes
Mathematics &
Computer Science
First-Year
CIP 27 - Mathematics
27.01 - General
27.03 - Applied
27.05 - Statistics
27.99 - Other
CIP 27 Total
CIP 11 - Computer
11.01 - General
11.02 - Programming
11.03 - Data Processing
11.04 - Info Science
11.05 - Computer System Analysis
11.06 - Data Entry
11.07 - Computer Science
11.08 - Software/Media Applications
11.09 - Networking and Telecom
11.10 - Comp/Info Management
11.99 - Support Service, other
CIP 11 Total
Six Years
CIP 27 - Mathematics
27.01 - General
27.03 - Applied
27.05 - Statistics
27.99 - Other
CIP 27 Total
CIP 11 - Computer
11.01 - General
11.02 - Programming
11.03 - Data Processing
11.04 - Info Science
11.05 - Computer System Analysis
11.06 - Data Entry
11.07 - Computer Science
11.08 - Software/Media Applications
11.09 - Networking and Telecom
11.10 - Comp/Info Management
11.99 - Support Service, other
CIP 11 Total
Transfer to 4-Year
STEM
Credits
Earned
%
224
Transfer to 4-Year
Non-STEM
Credits
Earned
%
Not
Transferred
Credits
Earned
%
35.1%
0.0%
4.2%
60.7%
100.0%
1,972
43
165
699
2,878
68.5%
1.5%
5.7%
24.3%
100.0%
4,548
112
129
858
5,647
80.5%
2.0%
2.3%
15.2%
100.0%
163
49
6
4
14
360
75
66
26
34
158
15.8%
24.6%
0.0%
0.0%
3.2%
20.3%
3.8%
6.3%
12.7%
13.3%
0.0%
100.0%
797
20.5%
6.1%
0.8%
0.5%
1.8%
45.1%
9.4%
8.3%
3.3%
4.3%
0.0%
100.0%
446
230
51
19
21
1,303
170
195
162
159
4
2,760
16.2%
8.3%
1.9%
0.7%
0.7%
47.2%
6.2%
7.1%
5.9%
5.8%
0.1%
100.0%
507
127
238
1,151
2,022
25.0%
6.3%
11.8%
56.9%
100.0%
3,477
213
985
2,011
6,687
52.0%
3.2%
14.7%
30.1%
100.0%
7,332
237
591
2,019
10,179
72.0%
2.3%
5.8%
19.8%
100.0%
89
277
22
3
42
96
94
69
91
68
3
853
10.4%
32.5%
2.6%
0.4%
4.9%
11.3%
11.0%
8.1%
10.6%
8.0%
0.4%
100.0%
501
206
52
19
28
957
151
295
92
163
6
2,470
20.3%
8.4%
2.1%
0.8%
1.1%
38.7%
6.1%
11.9%
3.7%
6.6%
0.2%
100.0%
869
795
150
25
81
3,024
286
761
417
802
10
7,220
12.0%
11.0%
2.1%
0.3%
1.1%
41.9%
4.0%
10.5%
5.8%
11.1%
0.1%
100.0%
27
388
639
25
39
5
32
6
10
20
21