Academic analytics is defined as the process of evaluating and analyzing organizational data received from
university
A university () is an institution of higher (or tertiary) education and research which awards academic degrees in several academic disciplines. Universities typically offer both undergraduate and postgraduate programs. In the United States, t ...
systems for reporting and decision making reasons (Campbell, & Oblinger, 200
Academic analytics will help student and faculty to track their career and professional paths. According to Campbell & Oblinger (2007), accrediting agencies,
Government, governments,
parents
A parent is a caregiver of the offspring in their own species. In humans, a parent is the caretaker of a child (where "child" refers to offspring, not necessarily age). A ''biological parent'' is a person whose gamete resulted in a child, a male t ...
and
students
A student is a person enrolled in a school or other educational institution.
In the United Kingdom and most commonwealth countries, a "student" attends a secondary school or higher (e.g., college or university); those in primary or elementary ...
are all calling for the adoption of new modern and efficient ways of improving and monitoring student success. This has ushered the higher education system into an era characterized by increased scrutiny from the various
stakeholders. For instance, the Bradley review acknowledges that
benchmarking
Benchmarking is the practice of comparing business processes and performance metrics to industry bests and best practices from other companies. Dimensions typically measured are quality, time and cost.
Benchmarking is used to measure performan ...
activities such as student engagement serve as indicators for gauging the institution's quality (Commonwealth
Government of Australia
The Australian Government, also known as the Commonwealth Government, is the national government of Australia, a federalism, federal parliamentary system, parliamentary constitutional monarchy. Like other Westminster system, Westminster-sty ...
, 2008).
Increased competition,
accreditation
Accreditation is the independent, third-party evaluation of a conformity assessment body (such as certification body, inspection body or laboratory) against recognised standards, conveying formal demonstration of its impartiality and competence to ...
, assessment and regulation are the major factors encouraging the adoption of analytics in
higher education
Higher education is tertiary education leading to award of an academic degree. Higher education, also called post-secondary education, third-level or tertiary education, is an optional final stage of formal learning that occurs after completi ...
. Although institutions of higher learning gather much vital data that can significantly aid in solving problems like attrition and retention, the collected data is not being analysed adequately and hence translated into useful data (Goldstein, 2005).
Subsequently,
higher education leadership are forced to make critical and vital decisions based on inadequate information that could be achieved by properly utilising and analysing the available data (Norris, Leonard, & strategic Initiatives Inc., 2008). This gives rise to strategic problems. This setback also depicts itself at the
tactical level
Military tactics encompasses the art of organizing and employing fighting forces on or near the battlefield. They involve the application of four battlefield functions which are closely related – kinetic or firepower, mobility, protection or se ...
. Learning and teaching at institutions of higher education if often a diverse and complex experience. Each and every teacher, student or course is quite different.
However,
LMS is tasked with taking care of them all. LMS is at the centre of academic analytics. It records each and every student and staff's information and results in a click within the system. When this crucial information is added, compared and contrasted with different enterprise information systems provides the institution with a vast array of useful information that can be harvested to gain a competitive edge (Dawson & McWilliam, 2008; Goldstein, 2005; Heathcoate & Dawson, 2005).
In order to retrieve meaningful information from institution sources i.e. LMS, the information has to be correctly interpreted against a basis of educational efficiency, and this action requires analysis from people with learning and teaching skills. Therefore, a collaborative approach is required from both the people guarding the data and those who will interpret it, otherwise the data will remain to be a total waste (Baepler & Murdoch, 2010). Decision making at its most basic level is based on
presumption
In the law of evidence, a presumption of a particular fact can be made without the aid of proof in some situations. The invocation of a presumption shifts the Legal burden of proof, burden of proof from one party to the opposing party in a court t ...
or
intuition
Intuition is the ability to acquire knowledge without recourse to conscious reasoning. Different fields use the word "intuition" in very different ways, including but not limited to: direct access to unconscious knowledge; unconscious cognition; ...
(a person can make conclusions and decisions based on experience without having to do data analysis) (Siemens & Long, 2011). However, a lot of decisions made at institutions of higher learning are too vital to be based on
anecdote
An anecdote is "a story with a point", such as to communicate an abstract idea about a person, place, or thing through the concrete details of a short narrative or to characterize by delineating a specific quirk or trait. Occasionally humorous ...
, presumption or intuition since significant decisions need to be backed by data and facts.
Background
Analytics
Analytics is the systematic computational analysis of data or statistics. It is used for the discovery, interpretation, and communication of meaningful patterns in data. It also entails applying data patterns toward effective decision-making. It ...
, which is often termed “business intelligence”, has come out as new
software
Software is a set of computer programs and associated documentation and data. This is in contrast to hardware, from which the system is built and which actually performs the work.
At the lowest programming level, executable code consists ...
and
hardware that enables businesses to gather and analyse large amounts of information or data. The analytics process is made up of gathering, analysing,
data manipulation
Statistics, when used in a misleading fashion, can trick the casual observer into believing something other than what the data shows. That is, a misuse of statistics occurs when a statistical argument asserts a falsehood. In some cases, the misus ...
and employing the results to answer critical questions such as ‘why’. Analytics was first applied in the admissions department in higher education institutions. The institutions normally used some formulas to choose students from a large pool of applicants. These formulas drew their information from high school transcripts and standardized test scores.
In today's world, analytics is commonly used in administrative units such as
fund raising
Fundraising or fund-raising is the process of seeking and gathering voluntary financial contributions by engaging individuals, businesses, charitable foundations, or governmental agencies. Although fundraising typically refers to efforts to gathe ...
and admissions. The use and application of academic analytics is meant to grow due to the ever-increasing concerns about student success and accountability. Academic analytics primarily marries complex and vast data with
predictive modelling
Predictive modelling uses statistics to predict outcomes. Most often the event one wants to predict is in the future, but predictive modelling can be applied to any type of unknown event, regardless of when it occurred. For example, predictive mod ...
and statistical techniques to better decision making. Current academic analytics initiatives are bent to use data to predict students experiencing difficulty (Arnold, & Pistilli, 2012, April). This allows advisors and faculty members to intervene by tailoring procedures which will meet the student's learning needs (Arnold, 2010).
As such, academic analytics possesses the ability to improve learning, student success and teaching. Analytics has become a valuable tool for institutions because of its ability to predict, model and improve decision making.
Analytic Steps
Analysis is composed of five basic steps: capture, report, predict, act and refine.
Capture: All analytic efforts are centred on data. Consequently, academic analytics can be rooted in data from various sources such as a
CMS
CMS may refer to:
Computing
* Call management system
* CMS-2 (programming language), used by the United States Navy
* Code Morphing Software, a technology used by Transmeta
* Collection management system for a museum collection
* Color manag ...
, and financial systems (Campbell, Finnegan, & Collins, 2006). Additionally, the data comes in various different formats for example
spread sheets. Also, data can be got from the institution's external environment. To capture data, academic analytics needs to determine the type of available data, methods of harnessing it and the formats it is in.
Report: After the data has been captured and stored in a central location, analysts will examine the data, perform
queries, identify patterns, trends and exceptions depicted by the data. The standard deviation and mean (
descriptive statistics
A descriptive statistic (in the count noun sense) is a summary statistic that quantitatively describes or summarizes features from a collection of information, while descriptive statistics (in the mass noun sense) is the process of using and an ...
) are mostly generated.
Predict: After analysing the warehoused data through the use of statistics, a
predictive model
Predictive modelling uses statistics to predict outcomes. Most often the event one wants to predict is in the future, but predictive modelling can be applied to any type of unknown event, regardless of when it occurred. For example, predictive mod ...
is developed. These models vary depending on the question nature and type of data. To develop a
probability
Probability is the branch of mathematics concerning numerical descriptions of how likely an Event (probability theory), event is to occur, or how likely it is that a proposition is true. The probability of an event is a number between 0 and ...
, these models employ
statistical regression
Statistics (from German: ''Statistik'', "description of a state, a country") is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. In applying statistics to a scientific, industri ...
concepts and techniques. Predictions are made after the use of
statistical algorithms
Computational statistics, or statistical computing, is the bond between statistics and computer science. It means statistical methods that are enabled by using computational methods. It is the area of computational science (or scientific computin ...
.
Act: The major goal and aim of analytics is to enable the institution to take actions based on the probabilities and
predictions
A prediction (Latin ''præ-'', "before," and ''dicere'', "to say"), or forecast, is a statement about a future event or data. They are often, but not always, based upon experience or knowledge. There is no universal agreement about the exact ...
made. These actions might vary from
invention
An invention is a unique or novel device, method, composition, idea or process. An invention may be an improvement upon a machine, product, or process for increasing efficiency or lowering cost. It may also be an entirely new concept. If an i ...
to information. The interventions to address problems might be in the form of a personal email, phone call or an automated contact from faculty advisors about study resources and skills, such as office hours or help sessions. Undoubtedly, institutions have to come up with appropriate mechanisms for impact measurement; such as did the students actually respond or attend the help sessions when invited.
Refine: Academic analytics should also be made up of a process aimed at
self-improvement
Self-help or self-improvement is a self-guided improvement''APA Dictionary of Physicology'', 1st ed., Gary R. VandenBos, ed., Washington: American Psychological Association, 2007.—economically, intellectually, or emotionally—often with a subst ...
. Statistics processes should be continually updated since the measurement of project impacts is not a one-time static effort but rather a continual effort. For instance, admission analytics should be updated or revised yearly.
Comprehending Involved Stakeholders
Analytics affects
executive officers, students, faculty members,
IT staff and student affairs staff. Whereas students will be keen to know academic analytics will affect their grades, faculty members will be interested in finding out how the information and data can be appropriated for other purposes (Pistilli, Arnold & Bethune, 2012). Moreover, the institution staff will be focussed on finding how the analysis will enable them to effectively accomplish their jobs while the institution president will be focussed on freshman retention and increase in graduation rates.
Criticisms
Analytics have been criticised for various reasons such as
profiling. Their main use is to profile students into successful and unsuccessful categories. However, some individuals argue that profiling of students tends to
bias
Bias is a disproportionate weight ''in favor of'' or ''against'' an idea or thing, usually in a way that is closed-minded, prejudicial, or unfair. Biases can be innate or learned. People may develop biases for or against an individual, a group, ...
people's behaviours and expectations (Ferguson, 2012). Additionally, there is no clear guidelines on which profiling issues should be prohibited or allowed in institutions of higher learning.
References
Academic Analyticsin the
EDUCAUSE
Educause is a nonprofit association in the United States whose mission is "to advance higher education through the use of information technology". Membership is open to institutions of higher education, corporations serving the higher education in ...
Resource Library
* Arnold, K. E. (2010). Signals: Applying Academic Analytics. Educause Quarterly, 33(1), n1. (accountability)
* Arnold, K. E., & Pistilli, M. D. (2012, April). Course Signals at Purdue: Using learning analytics to increase student success. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 267–270). ACM.
* Baepler, P., & Murdoch, C. J. (2010). Academic analytics and data mining in higher education. International Journal for the Scholarship of Teaching and Learning, 4(2), 17.
* Campbell, J. P., & Oblinger, D. G. (2007). Academic Analytics. Educause Article.
* Campbell, J. P., Finnegan, C., & Collins, B. (2006). Academic analytics: Using the CMS as an early warning system. In WebCT impact conference.
* Commonwealth Government of Australia. (2008). Review of Australian Higher Education o. Document Number)
* Dawson, S., & McWilliam, E. (2008). Investigating the application of IT generated data as an indicator of learning and teaching performance: Queensland University of Technology and the University of British Columbia. (A. L. a. T. Council o. Document Number)
* Ferguson, R. (2012). Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5), 304-317.
* Goldstein, P. (2005). Academic analytics: The uses of management information and technology in Higher Education o. Document Number)
* Heathcoate, L., & Dawson, S. (2005). Data Mining for Evaluation, Benchmarking and Reflective Practice in a LMS. E-Learn 2005: World conference on E-Learning in corporate, government, healthcare and higher education.
* Norris, D. M., Leonard, J., & Strategic Initiatives Inc. (2008). What Every Campus Leader Needs to Know About Analytics o. Document Number)
* Pistilli, M. D., Arnold, K., & Bethune, M. (2012). Signals: Using academic analytics to promote student success. EDUCAUSE Review Online, 1-8.
* Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. Educause Review, 46(5), 30-32.
References
{{Reflist
Business intelligence