=Paper=
{{Paper
|id=Vol-1905/recsys2017_poster12
|storemode=property
|title=A Recommender System for Personalized Exploration of Majors, Minors, and Concentrations
|pdfUrl=https://ceur-ws.org/Vol-1905/recsys2017_poster12.pdf
|volume=Vol-1905
|authors=Young Park
|dblpUrl=https://dblp.org/rec/conf/recsys/Park17
}}
==A Recommender System for Personalized Exploration of Majors, Minors, and Concentrations==
https://ceur-ws.org/Vol-1905/recsys2017_poster12.pdf
A Recommender System for Personalized Exploration of
Majors, Minors, and Concentrations*
Young Park
Department of Computer Science & Information Systems
Bradley University
U.S.A.
young@bradley.edu
ABSTRACT individual students better find their ideal majors, minors, and
concentrations and eventually achieve their academic goals.
Many students change their majors during college. Choosing the right
Recommender systems provide personalized recommendations
academic majors, minors, and concentrations within a major in higher
education is challenging but crucial in assuring students’ overall academic of items, such as goods, services, or information, that are most
and career success. Personalized prediction of student success in majors, likely to be relevant and interesting to users to help them find the
minors, and concentrations will help students better find the right majors, items most useful to them [2,3]. Recommender technologies have
minors, and concentrations for them, so as to timely achieve their academic been applied in a variety of application domains including the
goals. In this paper, we present a new recommender application for the traditional e-commerce domain as well as emerging domains such
academic major/minor/concentration selection problem in the educational as education and engineering. A number of recommender systems
domain. The proposed major/minor/concentration recommender system is have been developed and deployed in the educational domain for
a collaborative filtering-based recommender based on student grade
learning, teaching, and academic advising [4,5]. In the educational
prediction, and provides a variety of personalized predictions and
recommendations on majors, minors, and concentrations. It is a useful tool setting, the typical users are students, teachers, or academic
to guide students, advisors, and administrators during the personalized advisors, and the typical recommendable items are educational
major/minor/concentration exploration (declare/change) process. goods, services, or information such as courses, learning materials,
topics, student performance, and grades.
KEYWORDS In this paper, we describe a new educational recommender
Majors, minors, and concentrations; Recommender systems; application in order to help guide the college
Collaborative filtering; Personalized grade prediction major/minor/concentration exploration and selection process. The
proposed recommender can be used as a tool for students in the
major/minor/concentration exploration (declare/change) process
1 INTRODUCTION by providing personalized predictions of success in majors, minors,
and concentrations, along with recommendations for individual
Selecting the right academic major, minor, and/or concentration
students. The proposed tool can also be used by administrators and
within a major in higher education is challenging for many
advisors to guide decision-making and improve the academic
students. However, such selection can be crucial in assuring
experience for students at their educational institutions.
students’ overall academic success and later career satisfaction and
success. Many students change their majors over the course of their
college career. Students who pick the right major, minor, and/or 2 PERSONALIZED EXPLORATION OF
concentration tend to do better in the requisite courses and thus get MAJORS/MINORS/CONCENTRATIONS
better grades. Those students are also less likely to change their
chosen major, minor, and/or concentration or drop out, and thus are 2.1 Our Approach
more likely to graduate on time. Our goal is to predict how well a student will perform in majors,
Students typically choose their major, minor, and/or minors, and/or concentrations, i.e.
concentration based on various factors such as their interests, • Given a student and a major, minor, and/or concentration, how will
personality, and other considerations [1]. However, we believe that the student perform?
it is also very important to consider how students will perform in • Given a student, in what majors, minors, and/or concentrations will
that student perform well?
their selected major, minor, and/or concentration. Providing
An academic major, minor, or concentration typically includes
students with personalized prediction of success in majors, minors,
course requirements consisting of core courses and elective
and concentrations during the exploration process will greatly help
courses. A close approximation of a student’s performance can be
*
Copyright is held by the owner/author(s).
RecSys 2017 Poster Proceedings, August 27-31, Como, Italy.
ACM RecSys’17, August 2017, Como, Italy Y. Park
derived from the grades earned in his or her courses. Thus, we student at that time and (2) compute the grade point average of the
represent a student’s performance in a major, minor, or (m+n) courses in the sets in each pair in CMMC × EMMC.
concentration as the grades earned in both core and elective courses • Given only a target student S, (1) identify the sets CMMC and EMMC
for each potential major, minor, or concentration MMC, (2) predict
required in the major, minor, or concentration. Collaborative
the personalized grades for courses in the sets in each pair in C MMC
filtering recommendation techniques were applied to predict x EMMC that have not taken by the student at that time, and (3)
student grades in courses [6,7]. compute the grade point average of the (m+n) courses in the sets in
Our approach to major/minor/concentration recommendation is each pair in CMMC × EMMC.
based on personalized student-course grade prediction. From our myMMCheck can provide a variety of personalized predictions
previous work, we developed a collaborative-filtering-based and recommendations on majors, minors, and concentrations:
recommender system called pGPA that predicts personalized • For a student with a specific major, minor, or concentration selected
student grades [6]. Given a student, a course and a semester/term, by the student, myMMCheck predicts how well the student will
pGPA provides the student’s predicted grade in the course and the perform in that major, minor, or concentration by predicting the best
grade prediction proved accurate enough to support high-level overall grade point average along with the list of predicted grades in
m core courses and n elective courses. The student will have a better
decision making through experimentation on actual student grade
perspective on the chosen major, minor, or concentration, and thus
data. pGPA uses user-to-user collaborative filtering, item-to-item can better prepare and perform.
collaborative filtering, and matrix factorization for personalized • For a student, myMMCheck recommends a list of majors, minors, or
course grade predictions. To the best of our knowledge, our concentrations along with the set of m core courses and n elective
personalized major/minor/concentration recommender is the first courses in which the student will do well based on the overall grade
application of collaborative filtering to the point average predicted. The student can further explore the
major/minor/concentration exploration problem. recommended majors, minors, or concentrations.
2.2 A Major/Minor/Concentration 3 CONCLUSION AND FUTURE WORK
Recommender We have presented a novel recommender application to the
The proposed major/minor/concentration recommender system personalized major/minor/concentration exploration and selection
called myMMCheck is a collaborative filtering-based recommender problem. The proposed educational recommender system is based
system using the personalized grade prediction system pGPA [6]. on a collaborative filtering-based personalized grade prediction
system. The proposed recommender can provide a variety of
personalized tips on majors, minors, and concentrations and be
useful as a guiding tool during the personalized
major/minor/concentration exploration (declare/change) process.
As future work, we plan to build a real-world dataset and
perform comprehensive testing across curriculums in a higher
educational institution. We also plan to incorporate other important
factors besides students’ performance, including their interests,
personalities, and career goals.
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• Given a target student S and a specific major, minor, or
concentration MMC, (1) predict the personalized grades for courses
in the sets in each pair in CMMC x EMMC that have not taken by the