Analytics in higher education

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Academic analytics is defined as the process of evaluating and analyzing organizational data received from university systems for reporting and decision making reasons (Campbell, & Oblinger, 2007). Academic analytics will help student and faculty to track their career and professional paths. According to Campbell & Oblinger (2007), accrediting agencies, governments, parents and students 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 activities such as student engagement serve as indicators for gauging the institution's quality (Commonwealth Government of Australia, 2008).

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Increased competition, accreditation, assessment and regulation are the major factors encouraging the adoption of analytics in higher education. 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. 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). [1] Decision making at its most basic level is based on presumption or intuition (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, presumption or intuition since significant decisions need to be backed by data and facts.

Background

Analytics, which is often termed “business intelligence”, has come out as new software 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 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 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 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). [2] This allows advisors and faculty members to intervene by tailoring procedures which will meet the student's learning needs (Arnold, 2010). [3] 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, 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) are mostly generated.

Predict: After analysing the warehoused data through the use of statistics, a predictive model is developed. These models vary depending on the question nature and type of data. To develop a probability, these models employ statistical regression concepts and techniques. Predictions are made after the use of statistical algorithms.

Act: The major goal and aim of analytics is to enable the institution to take actions based on the probabilities and predictions made. These actions might vary from invention 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. 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 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.

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Computer lab

A computer lab is a space where computer services are provided to a defined community. These are typically public libraries and academic institutions. Generally, users must follow a certain user policy to retain access to the computers. This usually consists of rules such as no illegal activity during use or attempts to circumvent any security or content-control software while using the computers. Computer labs are often subject to time limits, this is to allow more people have a chance to use the lab. It is also common for personal login credentials to be required for access. This allows institutions to track the user's activities for any possible fraudulent use. The computers in computer labs are typically equipped with internet access, scanners, and printers and are typically arranged in rows. This is to give the workstation a similar view to facilitate lecturing or presentations, and also to facilitate small group work. For some academic institutions, student laptops or laptop carts take place of dedicated computer labs. However, computer labs still have a place in applications requiring special software or hardware which are not easily accessible in personal computers.

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 towards effective decision-making. It can be valuable in areas rich with recorded information; analytics relies on the simultaneous application of statistics, computer programming and operations research to quantify performance.

A learning management system (LMS) is a software application for the administration, documentation, tracking, reporting, automation, and delivery of educational courses, training programs, or learning and development programs. The learning management system concept emerged directly from e-Learning. Learning management systems make up the largest segment of the learning system market. The first introduction of the LMS was in the late 1990s. Learning management systems have faced a massive growth in usage due to the emphasis on remote learning during the COVID-19 pandemic.

A learning community is a group of people who share common academic goals and attitudes and meet semi-regularly to collaborate on classwork. Such communities have become the template for a cohort-based, interdisciplinary approach to higher education. This may be based on an advanced kind of educational or 'pedagogical' design.

The National Survey of Student Engagement (NSSE) is a survey mechanism used to measure the level of student participation at universities and colleges in Canada and the United States as it relates to learning and engagement. The results of the survey help administrators and professors to assess their students' student engagement. The survey targets first-year and senior students on campuses. NSSE developed ten student Engagement Indicators (EIs) that are categorized in four general themes: academic challenge, learning with peers, experiences with faculty, and campus environment. Since 2000, there have been over 1,600 colleges and universities that have opted to participate in the survey. Additionally, approximately 5 million students within those institutions have completed the engagement survey. Overall, NSSE assesses effective teaching practices and student engagement in educationally purposeful activities. The survey is administered and assessed by Indiana University School of Education Center for Postsecondary Research.

A course evaluation is a paper or electronic questionnaire, which requires a written or selected response answer to a series of questions in order to evaluate the instruction of a given course. The term may also refer to the completed survey form or a summary of responses to questionnaires.

Student engagement occurs when "students make a psychological investment in learning. They try hard to learn what school offers. They take pride not simply in earning the formal indicators of success (grades), but in understanding the material and incorporating or internalizing it in their lives." Since the U.S. college dropout rate for first-time-in college degree-seeking students is nearly 50% It is increasingly seen as an indicator of successful classroom instruction, and as a valued outcome of school reform. The phrase was identified in 1996 as "the latest buzzword in education circles." Students are engaged when they are involved in their work, persist despite challenges and obstacles, and take visible delight in accomplishing their work. Student engagement also refers to a "student's willingness, need, desire and compulsion to participate in, and be successful in, the learning process promoting higher level thinking for enduring understanding." Student engagement is also a usefully ambiguous term for the complexity of 'engagement' beyond the fragmented domains of cognition, behaviour, emotion or affect, and in doing so encompass the historically situated individual within their contextual variables that at every moment influence how engaged an individual is in their learning.

University student retention, sometimes referred to as persistence, is of increasing importance to college administrators as they try to improve graduation rates and decrease a loss of tuition revenue from students that either drop out or transfer to another school. The topic is also of high importance to students, who invest their time and resources in support of the hope of earning a degree.

Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs. The growth of online learning since the 1990s, particularly in higher education, has contributed to the advancement of Learning Analytics as student data can be captured and made available for analysis. When learners use an LMS, social media, or similar online tools, their clicks, navigation patterns, time on task, social networks, information flow, and concept development through discussions can be tracked. The rapid development of massive open online courses (MOOCs) offers additional data for researchers to evaluate teaching and learning in online environments.

Educational data mining (EDM) describes a research field concerned with the application of data mining, machine learning and statistics to information generated from educational settings. At a high level, the field seeks to develop and improve methods for exploring this data, which often has multiple levels of meaningful hierarchy, in order to discover new insights about how people learn in the context of such settings. In doing so, EDM has contributed to theories of learning investigated by researchers in educational psychology and the learning sciences. The field is closely tied to that of learning analytics, and the two have been compared and contrasted.

The CUNY Academic Commons is an online, academic social network for community members of the City University of New York (CUNY) system. Designed to foster conversation, collaboration, and connections among the 24 individual colleges that make up the university system, the site, founded in 2009, has quickly grown as a hub for the CUNY community, serving in the process to strengthen a growing group of digital scholars, teachers, and open-source projects at the university.

A virtual learning environment (VLE) in educational technology is a web-based platform for the digital aspects of courses of study, usually within educational institutions. They present resources, activities, and interactions within a course structure and provide for the different stages of assessment. VLEs also usually report on participation; and have some level of integration with other institutional systems.

MERLOT is an online repository and international consortium of institutions of higher education, industry partners, professional organizations and individuals. MERLOT partners and members are devoted to identifying, peer reviewing, organizing and making available existing online learning resources in a range of academic disciplines for use by higher education faculty and students.

The Horizon Project is an initiative by EDUCAUSE to chart emerging technologies and trends impacting the future of higher education across domains such as teaching and learning and information security. Drawing on insights from a global panel of leaders from across the higher education landscape, the objective of each Horizon Report is to shape decision-making among higher education professionals by helping them imagine a range of possible futures and think through the present-day implications of those futures. The Horizon Project was launched in 2002 by Laurence F. Johnson, CEO of NMC, and since the 2018 edition has been published by EDUCAUSE.

Ankara Yıldırım Beyazıt University Public university in Çubuk, Ankara, Turkey

Ankara Yıldırım Beyazıt University (AYBU), founded in 2010, is a distinguished state higher education institution in Ankara, Turkey that prides itself on its strong international perspective, high quality research-based education and diversity.

Unizin is both a consortium of higher education institutions and a service provider. The Unizin consortium was founded in 2014 by Colorado State University, University of Florida, Indiana University, and University of Michigan. On July 22, 2014, Unizin named Amin Qazi its founding CEO. The Unizin service debuted its first offering, Canvas by Instructure, in late summer 2014. The goal of the Unizin service is to establish the standard gauge rails of digital learning, creating common standards that enable collaboration within the higher education community. The Unizin consortium offers a channel for collaborating on solutions to the many challenges being faced by educational institutions, as well as a means for those institutions to collectively govern resources and cost-effectively control infrastructure necessary to enable innovation at their universities. Unizin, Ltd. is a registered 501(c)(3).

Higher education in Myanmar

Higher education in Myanmar has experienced a large expansion since 1988, although ranks as one of the lowest globally for universities. Due to the student protests in the 8888 uprising, the Myanmar government closed down all universities for two years. Additional student protests in 1996 and 1998 caused all universities to be closed for another three years.

A learning relationship management (LRM) software system manages and facilitates student-led instruction to maximize student engagement, achievement, outcome and long-term success. Unlike learning management systems (LMS) in which elements are organized around specific courses, LRMs are student-centric in design, facilitate personalized learning, and provide individualized learning paths, a central point for analytics data and a way of tracking interventions and related results. The LRM system provides a comprehensive foundation for end-to-end student support", which may include communication with and/or support from a learner network consisting of educators, administrators, parents/guardians, mentors, advisors/guidance counselors, etc.

Online learning in higher education Development in distance education that began in the mid-1980s

Online learning involves courses offered by primary institutions that are 100% virtual. Online learning, or virtual classes offered over the internet, is contrasted with traditional courses taken in a brick-and-mortar school building. It is a development in distance education that expanded in the 1990s with the spread of the commercial Internet and the World Wide Web. The learner experience is typically asynchronous but may also incorporate synchronous elements. The vast majority of institutions utilize a learning management system for the administration of online courses. As theories of distance education evolve, digital technologies to support learning and pedagogy continue to transform as well.

The Campus Privacy Officer (CPO) is a position within a post-secondary university that ensures that student, faculty, and parent privacy is maintained. The CPO role was created because of growing privacy concerns across college campuses. The responsibilities of the CPO vary depending on the specific needs of the campus community. Their daily tasks may include drafting new privacy policies for their respective college campus, creating a curriculum that informs teachers and students about privacy, helping to investigate any privacy breaches within the university, and ensuring that the university is abiding by current state and federal privacy laws. CPOs are also responsible for connecting with student and faculty groups across the entire campus in order to understand the privacy concerns of the campus. The role of CPO is an expanding profession within the United States and other countries, such as Canada and South Africa. There are numerous organizations that exist to provide training for CPOs and support them.

References

References

  1. Baepler, Paul; Murdoch, Cynthia James (July 2010). "Academic Analytics and Data Mining in Higher Education". International Journal for the Scholarship of Teaching and Learning. 4 (2). Article 17. doi: 10.20429/ijsotl.2010.040217 . S2CID   8688376.
  2. "Course Signals at Purdue: Using Learning Analytics to Increase Student Success". LACE Evidence Hub. Retrieved 2020-04-05.
  3. "Signals: Applying Academic Analytics". er.educause.edu. Retrieved 2020-04-05.