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Development of Quantitative Skills Course for College Students

Write a four to six (4-6) page paper in which you:

Provide a description of a course that you plan to develop for this class, including at least three (3) goals

of the course.

Describe the target learners, discussing at least three (3) unique needs and / or challenges the online

learners face.�

Develop six (6) learning outcomes for the course.

Propose the primary learning theory or theories you plan to apply to ensure students achieve the

outcomes, providing a rationale for the theory or theories.

Propose three (3) ways you plan to motivate students in the online environment, providing a rationale for

each way. �

�Provide at least three (3) academic references (published within the last five [5] years) to support your


Development of Quantitative Skills Course for College Students


Course Description
The importance of learning quantitative skills for university students is underpinned by
the need to equip graduates with skills that will enable them to perform effectively in
professional environments that are increasingly driven by computerized technologies and
research data. Students undertaking higher education in science disciplines require a strong
background in mathematics. Many students entering university usually need a refresher course in
some of the foundational topics that have been learned in their past years of schooling. This
course aims at teaching the foundation topics in quantitative research methods that are
commonly covered in many undergraduate courses that employ scientific research. The course
aims at providing basic training in statistical research methods in topics that provide a basic
understanding of the concepts and principles applied in quantitative research methodologies as
well as enable learners acquire skills for practical research in social sciences, education, business
and humanities fields. Students will be expected to develop competences in quantitative and
statistical techniques through hands-on practice working on research design, data collection, and
data management as well as the analysis and interpretation of field data.


In this course, students will get a basic understanding of basic concepts in mathematics as
well as core quantitative skills that are required for learners in operating with research data and
computer-based statistical methods. Students will develop an understanding of mathematical
computation applied in research, which will … *** confidence in applying basic statistical
methods for scientific research. Students will learn important topics for statistical analysis
including basic statistical formula and concepts for testing research hypotheses. The skills
acquired will enable students to apply basic statistical tests in analyzing research data, which will
allow students to build a strong foundation necessary to proceed to more advanced concepts
taught at the university in their respective professional disciplines. This course is intended to
enable students … *** courses that will get them to the prerequisite beginner level necessary for
learning more advanced units in mathematics taught in the university. The topics covered in this
course will include basic statistical concepts including quantitative research design, data
collection and tabulation, data management using statistical software, mathematical concepts for
comparing statistical significance and for hypothesis testing, and interpretation of statistical
descriptors of findings. Students will also learn statistical methods for simple and multiple
regression, scale reliability, contingency analysis, and critical appraisal of research findings
(Rock, Coventry, Morgan and Loi, 2016).
Target Learners
This course will target students that require an introductory understanding in the use of
statistics for conducting empirical research relating to their respective professional disciplines.
The target learners for this course are students entering university in their first year of college,
and second year students undertaking courses in social sciences, education, business studies and
humanities that will utilize statistical research methods to examine various phenomena


encountered in these professions. Emphasis will be placed on enabling students achieve a basic
understanding of the statistical procedures used in practical research. The students targeted in
this course should be able to think critically about the most suitable procedures for collecting and
analyzing research data, and to appreciate the educational usefulness of statistical calculations
using computer software packages (Tishkovskaya and Lancaster, 2012).

Learning Outcomes
Upon completion of this course, students are expected to demonstrate basic knowledge
and understanding of the various quantitative methods and concepts for conducting research in
practical application of more advanced concepts taught in their respective courses. Students
completing this course are expected to be able to systematically appraise and review statistical
and methodological issues faced in conducting quantitative research studies. Students will be
expected to apply basic quantitative research techniques for data management, analysis and
review. Students will be expected to understand the use of different computer software programs
such as SPSS and Microsoft Excel to manage and analyze research data. Students completing the
course will be expected to demonstrate skills in critical thinking and problem solving that is
steeped in research methods and rigor in statistical analysis of information based on empirical
evidence collected using a scientific approach to development of academic knowledge and
theory (Baglin, 2013). Students will further be expected to be able to present numerical and
statistical research findings in a report format that is commonly used for presenting information
in professional research studies.
Primary Learning Theories Applied in Teaching the Course


The primary learning theory proposed for teaching this course is the cognitive
psychology theory that proposes that individuals learn best when they practice what they learn
and apply the concepts learned in practical situations. According to the theory, knowledge tends
to be directly related to the context in which it is taught and learned, and that learning is most
effective when students can receive real-time feedback on errors through direct application of
learned knowledge in solving problems. The theory recognizes that learning becomes less
effective as the mental load for students increases. Teachers must therefore strive to integrate the
new knowledge being taught with the existing knowledge in an incremental improvement of
skills being taught (Slootmaeckers, Adriaensen and Kerremans, 2012).
Learning Environment
The course will be taught online in a learning environment that provides students with
opportunities for practical application of concepts learned. The course instructor will seek to
establish a balance between a ‘teacher centered’ and ‘student centered’ approach to learning with
emphasis providing a learning pedagogy that will engage students in integrating theoretical
knowledge to practical situations (Baglin, 2013). Besides the various lecture and reading
materials provided for the course, the instructor will make available chapters from statistic
textbooks selected for enabling students to acquire the theoretical concepts being taught in the
course. The lecture notes and readings will be supported by tests where students will solve
questions relating to theoretical and practical aspects of research. Students will be required to
apply theoretical concepts to practical research situations. The students will be required to apply
their learned knowledge to search for background theoretical information and field data for use
in solving the problems. The learners are assisted by the instructor in searching for available field
data for research and to choose amongst appropriate and relevant statistical procedures for


undertaking research. The students will also be required to justify and support the particular
strategies used in tackling practical research problems, and to present their research findings in a
report format that is acceptable for professional standards (Slootmaeckers, Adriaensen and
Kerremans, 2012). Students will also be provided with opportunities for using their acquired
knowledge in quantitative research to undertake practical sessions in problem-solving for
statistical research using computer software programs, which will require the active participation
of students in computer-aided research workshops.



Baglin, J. (2013). “Evaluating learning theory-based methods for improving the learning
outcomes of introductory statistics courses”, Doctor of Philosophy (PhD), Mathematical
and Geospatial Sciences, RMIT University.
Rock, Adam J., Coventry, William L., Morgan, Methuen I. and Loi, Natasha M. (2016).
“Teaching Research Methods and Statistics in eLearning Environments: Pedagogy,
Practical Examples, and Possible Futures”, Frontiers in Psychology, 7(1), 339.

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