Running head: LITERATURE_REVIEW_DAVIS
Literature Review:
Curriculum Minds: Using Learning Analytics to “Profile Success”
Deborah Davis
Liberty University Online
EDUC 730: Instructional Technology
Dr. Dave Holder
June 10, 2015
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Abstract
This review seeks to determine if the use of Learning Analytics (LA) can aid educators in
predicting the ongoing needs of adult students as they work through their college education.
Adult students, particularly young freshmen, have a tendency to be wandering through the
wealth of information found in the college catalog. If courses, classes, programs, and study
methods are trees, these students can easily be lost in the woods and fall victim to the tanglefoot
of opportunities that abound. Learning Analytics allow for the compilation of each student’s
historical data, personality traits and aptitudes, and can predict pitfalls and dead-ends on that
student’s path. Prescriptive solutions to redirect the student can lead to a more successful college
experience.
Keywords: Learning Analytics, Adult Education, Freshmen College Students
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Curriculum Minds: Using Learning Analytics to “Profile Success”
If ignorance were a crime, many of our students would be victims. Low performance on
tests, students who dread learning, and teachers who struggle to encourage students and placate
or engage parents provide evidence of this heinous act. The unknown subject (UnSub) is the
direction the students need to succeed at the college level. As we embark on this investigation,
we must remember Nelson Mandela’s words: “Education is the most powerful weapon you can
use to change the world.”
Reflective moment
Experienced teachers are able to take a few days of experience with a student, a couple of
assignments, and make a prediction about that student’s outcome. While not a perfect science, it
is the education, experience, and instinct of that teacher to filter out the needs of each student.
With the accumulation of more than 25 years of teaching, in my recent personal experience of 12
years as a Freshman College Composition Adjunct Faculty member, I have an 88.8% success
rate (+/- .01) at predicting final grades after the first two weeks of class. For the experienced
teacher, this becomes a matter of practice. While I set aside my predictions until after class, I am
always thrilled to find myself able to be wrong about those students who would do poorly. As
the class progresses, I am able to isolate needs of certain students and to teach them how to
delineate requirements in a way that best suits them. This allows them to succeed more readily
in my class and in any others they may have.
Literature Review
Defining Learning Analytics
“According to Siemens (2011) LA is ‘the measurement, collection, analysis and reporting
of data about learners and their contexts, for purposes of understanding and optimising [sic]
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learning and the environments in which it occurs . . . ’ Making use of learning analytics can give
added value to learners as well as educators” (Scheffel, Drachsler, Stoyanov, & Specht, 2014, p.
117).
Big Data – History, Protection, Ethical Use
This study indicates that stakeholders of students with autism spectrum disorders (ASD)
are not generally included early enough in the research process to make the research practicable
(Dingfelder & Mandell, 2011). Consequently, whatever results are applied can seldom be
implemented at the real-world level. Including stakeholders early in the process will enhance
real-world applicability to the research, and make it feasible. At the collegiate level, it is crucial
that stakeholders be engaged in any changes to the process of remedial English courses – from
selection for, through completion of – to make the changes practicable.
Traits and Aptitudes Converted to Data
“People have different characteristics which affect their life affairs; even the way they
learn is influenced by these personal characteristics” (Boroujeni, A., Roohani, A., &
Hasanimanesh, A., 2015, p. 212). It would seem a given that intelligence is directly related to
school performance, but we know that is not always true. We also know that there are noncognitive and non-ability relationships to school performance. Most practitioners are familiar
the “Big-Five” personality traits – Neuroticism, Extraversion, Openness to Experience,
Agreeableness, and Conscientiousness (Furnham, Monsen, & Ahmetoglu, 2009, p. 770). The
ability to catalog personality allows for the integration of intuitive traits and personality
characteristics into Learning Analytics (LA) and thus allow for predictions of pitfalls, and how to
avoid them.
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Students generally know themselves. They can identify their own learning approaches as
deep-learning or surface-learning, as well as personality attributes of extraversion and
neuroticism, and compensating for gender and if level of learning is beginning or advanced
(Ngidi, 2013).
Predictive Behaviors
In a shift of focus from the students, Dallas, Upton, and Sprong (2014) focused on faculty
providing inclusive curriculum to accommodate for the needs of all students. The inclusive
method in this article is referred to as Universal Design Instruction (UDI) (p. 12). This study
attempted to measure the attitudes of the faculty members toward implementation of UDI within
their various course structures. The bottom line in this study was that while most faculty were
open to multiple methods to accommodate LD students, those with more training in special
education were more forthcoming and had better attitudes toward inclusivity than others.
Prescriptive Actions
Conclusion
All students need support. Learning Analytics allow us, as educators, to better direct the
paths of our students. In doing so, we keep them safe from the ignorance that surrounds them,
and give them tools to defend themselves from it. The hazards and pitfalls that abound those
freshmen students can be ameliorated through the adept educator’s uuse of the tools at hand.
Negative predictions can fail is we, the teachers, and they, the students, work together using tools
like LA to keep them safely striving toward success.
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References
Furnham, A., Monsen, J., & Ahmetoglu, G. (2009). Typical intellectual engagement, Big Five
personality traits, approaches to learning and cognitive ability predictors of academic
performance. The British Journal Of Educational Psychology, 79(Pt 4), 769-782.
doi:10.1348/978185409X412147
Boroujeni, A., Roohani, A., & Hasanimanesh, A. (2015). The Impact of Extroversion and
Introversion Personality Types on EFL Learners' Writing Ability. Theory & Practice In
Language Studies, 5(1), 212-218. doi:10.17507/tpls.0501.29
Ngidi, D. P. (2013). Students' personality traits and learning approaches. Journal Of Psychology
In Africa, 23(1), 149-152.
Scheffel, M., Drachsler, H., Stoyanov, S., & Specht, M. (2014). Quality Indicators for Learning
Analytics. Journal Of Educational Technology & Society, 17(4), 117-132.