Predictive analytic models of student success in higher education

2019 ◽  
Vol 120 (3/4) ◽  
pp. 208-227 ◽  
Author(s):  
Ying Cui ◽  
Fu Chen ◽  
Ali Shiri ◽  
Yaqin Fan
Keyword(s):  
Higher Education ◽  
Student Success ◽  
Learning Analytics ◽  
Model Development ◽  
Text Documents ◽  
Content Type ◽  
Predictive Learning ◽  
Search Terms ◽  
Methodological Considerations ◽  
Analytic Models

Purpose Many higher education institutions are investigating the possibility of developing predictive student success models that use different sources of data available to identify students that might be at risk of failing a course or program. The purpose of this paper is to review the methodological components related to the predictive models that have been developed or currently implemented in learning analytics applications in higher education. Design/methodology/approach Literature review was completed in three stages. First, the authors conducted searches and collected related full-text documents using various search terms and keywords. Second, they developed inclusion and exclusion criteria to identify the most relevant citations for the purpose of the current review. Third, they reviewed each document from the final compiled bibliography and focused on identifying information that was needed to answer the research questions Findings In this review, the authors identify methodological strengths and weaknesses of current predictive learning analytics applications and provide the most up-to-date recommendations on predictive model development, use and evaluation. The review results can inform important future areas of research that could strengthen the development of predictive learning analytics for the purpose of generating valuable feedback to students to help them succeed in higher education. Originality/value This review provides an overview of the methodological considerations for researchers and practitioners who are planning to develop or currently in the process of developing predictive student success models in the context of higher education.

scholarly journals Predictive learning analytics and the creation of emotionally adaptive learning environments in higher education institutions: a study of students' affect responses

2021 ◽  
Vol 38 (2) ◽  
pp. 243-257
Author(s):  
Paul Joseph-Richard ◽  
James Uhomoibhi ◽  
Andrew Jaffrey
Keyword(s):  
Higher Education ◽  
At Risk ◽  
Learning Analytics ◽  
Higher Education Institutions ◽  
Well Being ◽  
Small Sample ◽  
Small Scale ◽  
Affective States ◽  
Content Type ◽  
Predictive Learning

PurposeThe aims of this study are to examine affective responses of university students when viewing their own predictive learning analytics (PLA) dashboards, and to analyse how those responses are perceived to affect their self-regulated learning behaviour.Design/methodology/approachA total of 42 Northern Irish students were shown their own predicted status of academic achievement on a dashboard. A list of emotions along with definitions was provided and the respondents were instructed to verbalise them during the experience. Post-hoc walk-through conversations with participants further clarified their responses. Content analysis methods were used to categorise response patterns.FindingsThere is a significant variation in ways students respond to the predictions: they were curious and motivated, comforted and sceptical, confused and fearful and not interested and doubting the accuracy of predictions. The authors show that not all PLA-triggered affective states motivate students to act in desirable and productive ways.Research limitations/implicationsThis small-scale exploratory study was conducted in one higher education institution with a relatively small sample of students in one discipline. In addition to the many different categories of students included in the study, specific efforts were made to include “at-risk” students. However, none responded. A larger sample from a multi-disciplinary background that includes those who are categorised as “at-risk” could further enhance the understanding.Practical implicationsThe authors provide mixed evidence for students' openness to learn from predictive learning analytics scores. The implications of our study are not straightforward, except to proceed with caution, valuing benefits while ensuring that students' emotional well-being is protected through a mindful implementation of PLA systems.Social implicationsUnderstanding students' affect responses contributes to the quality of student support in higher education institutions. In the current era on online learning and increasing adaptation to living and learning online, the findings allow for the development of appropriate strategies for implementing affect-aware predictive learning analytics (PLA) systems.Originality/valueThe current study is unique in its research context, and in its examination of immediate affective states experienced by students who viewed their predicted scores, based on their own dynamic learning data, in their home institution. It brings out the complexities involved in implementing student-facing PLA dashboards in higher education institutions.

scholarly journals Virtual reality and gamification in marketing higher education: a review and research agenda

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sandra Maria Correia Loureiro ◽  
Ricardo Godinho Bilro ◽  
Fernando José de Aires Angelino
Keyword(s):  
Higher Education ◽  
Virtual Reality ◽  
Research Agenda ◽  
Research Field ◽  
Future Research ◽  
Research Topics ◽  
Content Type ◽  
Education Experience ◽  
Future Research Agenda ◽  
Search Terms

Purpose The purpose of this paper is to review studies on the use of virtual reality (VR) and gamification to engage students in higher education for marketing issues to identify the research topics, the research gaps and to prepare a future research agenda. Design/methodology/approach A literature review is performed based on two search terms applied to Web of Science, resulting in a final pool of 115 articles. A text-mining approach is used to conduct a full-text analysis of papers related to VR and gamification in higher education. The authors also compare the salient characteristics presented in the articles. Findings From this analysis, five major research topics are found and analysed, namely, teaching methodologies and education, experience and motivation, student engagement, applied theories in VR and gamification. Based on this and following the theory concept characteristics methodology framework, the paper provides directions for future research. Originality/value There is no comprehensive review exploring the topics, theories, constructs and methods used in prior studies concerning VR and gamification applied to higher education services based on all the articles published in well-regarded academic journals. This review seeks to provide deeper insights, to help scholars contribute to the development of this research field.

Navigating student success: learning from the higher education landscape

2019 ◽  
Vol 47 (3) ◽  
pp. 207-223
Author(s):  
Lynn Deeken ◽  
Meggan Press ◽  
Angie Thorpe Pusnik ◽  
Laura Birkenhauer ◽  
Nate Floyd ◽  
...  
Keyword(s):  
Higher Education ◽  
Strategic Planning ◽  
Student Success ◽  
Design Methodology ◽  
Institutions Of Higher Education ◽  
Content Type ◽  
Shared Interest

Purpose This paper aims to demonstrate the variety of ways institutions and their libraries approach student success both conceptionally and operationally. Design/methodology/approach Librarians from nine different institutions of higher education were given a series of questions about student success on their campuses and in their libraries. They responded with written essays describing their experiences and perspectives. Findings The contributed pieces are collected together and display a shared interest in defining “student success,” aligning strategic planning with student success initiatives and establishing (and assessing) strong infrastructure to support student success. Originality/value These examples help us observe what is happening throughout higher education and see potential paths forward at our own institutions engaged in this work.

scholarly journals A large-scale implementation of predictive learning analytics in higher education: the teachers’ role and perspective

2019 ◽  
Vol 67 (5) ◽  
pp. 1273-1306 ◽  
Author(s):  
Christothea Herodotou ◽  
Bart Rienties ◽  
Avinash Boroowa ◽  
Zdenek Zdrahal ◽  
Martin Hlosta
Keyword(s):  
Higher Education ◽  
Learning Analytics ◽  
Large Scale ◽  
Predictive Learning

Impacting Big Data analytics in higher education through Six Sigma techniques

2017 ◽  
Vol 66 (5) ◽  
pp. 662-679 ◽  
Author(s):  
Chad Laux ◽  
Na Li ◽  
Corey Seliger ◽  
John Springer
Keyword(s):  
Higher Education ◽  
Big Data ◽  
Student Success ◽  
Research Design ◽  
Six Sigma ◽  
Data Analytics ◽  
Design Methodology ◽  
Big Data Analytics ◽  
Content Type ◽  
Conceptual Research

Purpose The purpose of this paper is to develop a framework for utilizing Six Sigma (SS) principles and Big Data analytics at a US public university for the improvement of student success. This research utilizes findings from the Gallup index to identify performance factors of higher education. The goal is to offer a reimagined SS DMAIC methodology that incorporates Big Data principles. Design/methodology/approach The authors utilize a conceptual research design methodology based upon theory building consisting of discovery, description, explanation of the disciplines of SS and Big Data. Findings The authors have found that the interdisciplinary approach to SS and Big Data may be grounded in a framework that reimagines the define, measure, analyze, improve and control (DMAIC) methodology that incorporates Big Data principles. The authors offer propositions of SS DMAIC to be theory tested in subsequent study and offer the practitioner managing the performance of higher education institutions (HEIs) indicators and examples for managing the student success mission of the organization. Research limitations/implications The study is limited to conceptual research design with regard to the SS and Big Data interdisciplinary research. For performance management, this study is limited to HEIs and non-FERPA student data. Implications of this study include a detailed framework for conducting SS Big Data projects. Practical implications Devising a more effective management approach for higher education needs to be based upon student success and performance indicators that accurately measure and support the higher education mission. A proactive approach should utilize the data rich environment being generated. The individual that is most successful in engaging and managing this effort will have the knowledge and skills that are found in both SS and Big Data. Social implications HEIs have historically been significant contributors to the development of meritocracy in democratic societies. Due to a variety of factors, HEIs, especially publicly funded institutions, have been under stress due to a reduction of public funding, resulting in more limited access to the public in which they serve. Originality/value This paper examines Big Data and SS in interdisciplinary effort, an important contribution to SS but lacking a conceptual foundation in the literature. Higher education, as an industry, lacks penetration and adoption of continuous improvement efforts, despite being under tremendous cost pressures and ripe for disruption.

Strategic value realization framework from learning analytics: a practical approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Riyaz Abdullah Sheikh ◽  
Surbhi Bhatia ◽  
Sujit Gajananrao Metre ◽  
Ali Yahya A. Faqihi
Keyword(s):  
Higher Education ◽  
Learning Analytics ◽  
Skill Training ◽  
Quality Data ◽  
Future Research ◽  
Content Type ◽  
Data Infrastructure ◽  
Learning And Teaching ◽  
Strategic Value ◽  
Value Realization

PurposeIn spite of the popularity of learning analytics (LA) in higher education institutions (HEIs), the success rate and value gained through LA projects is still little and unclear. The existing research on LA focusses more on tactical capabilities rather than its effect on organizational value. The key questions are what are the expected benefits for the institution? And how the investment in LA can bring tangible value? In this research, the authors proposed a value realization framework from LA extending the existing framework of information technology value.Design/methodology/approachThe study includes a detailed literature review focusing on the importance, existing frameworks and LA adoption challenges. Based on the identified research gap, a new framework is designed. The framework depicts the several constructs and their relationships focusing on strategic value realization. Furthermore, this study includes three case studies to validate the framework.FindingsThe framework suggests that leveraging LA for strategic value demands adequate investment not only in data infrastructure and analytics but also in staff skill training and development and strategic planning. Universities are required to measure the strategic role of LA and spend wisely in quality data, analytical tools, skilled staff who are aware of the latest technologies and data-driven opportunities for continuous improvement in learning.Originality/valueThe framework permits education leaders to design better strategies for attaining excellence in learning and teaching, and furnish learners with new data to settle on the most ideal decisions about learning. The authors believe that the appropriation of this framework and consistent efficient interest in learning analytics by the higher education area will prompt better results for learners, colleges and more extensive society. The research also proposes two approaches and eleven research agendas for future research based on the framework. The first is based on the constructs and their relationships in LA value creation, whereas the later one focusing on identifying problems associate with it.

scholarly journals Learning analytics in higher education: an analysis of case studies

2017 ◽  
Vol 12 (1) ◽  
pp. 21-40 ◽  
Author(s):  
Billy Tak Ming Wong
Keyword(s):  
Higher Education ◽  
Case Studies ◽  
Learning Analytics ◽  
Research Work ◽  
Emotional States ◽  
Students At Risk ◽  
Content Type ◽  
Face To Face ◽  
Expected Benefits ◽  
Learning Behaviours

Purpose The purpose of this paper is to present a systematic review of the mounting research work on learning analytics. Design/methodology/approach This study collects and summarizes information on the use of learning analytics. It identifies how learning analytics has been used in the higher education sector, and the expected benefits for higher education institutions. Empirical research and case studies on learning analytics were collected, and the details of the studies were categorized, including their objectives, approaches, and major outcomes. Findings The results show the benefits of learning analytics, which help institutions to utilize available data effectively in decision making. Learning analytics can facilitate evaluation of the effectiveness of pedagogies and instructional designs for improvement, and help to monitor closely students’ learning and persistence, predict students’ performance, detect undesirable learning behaviours and emotional states, and identify students at risk, for taking prompt follow-up action and providing proper assistance to students. It can also provide students with insightful data about their learning characteristics and patterns, which can make their learning experiences more personal and engaging, and promote their reflection and improvement. Originality/value Despite being increasingly adopted in higher education, the existing literature on learning analytics has focussed mainly on conventional face-to-face institutions, and has yet to adequately address the context of open and distance education. The findings of this study enable educational organizations and academics, especially those in open and distance institutions, to keep abreast of this emerging field and have a foundation for further exploration of this area.

scholarly journals Unresolved Privacy and Ethics Issues Related to Learning Analytics in Higher Education and Academic Librarianship

2021 ◽  
Vol 4 (1) ◽  
pp. 117-142
Author(s):  
Chad Currier
Keyword(s):  
Higher Education ◽  
Student Success ◽  
Learning Analytics ◽  
Privacy Rights ◽  
Organizational Assessment ◽  
Privacy Concerns ◽  
Academic Librarianship ◽  
Personally Identifiable Information ◽  
Ethical And Legal Considerations ◽  
Collection And Management

Learning analytics involve big data collection, analysis processes, and technology that are used in higher education institutes and academic libraries to support student success and perform organizational assessment. Since these processes require the input of personally identifiable student and patron information to be effective, there are major ethical and legal considerations that must be addressed concerning privacy. This article demonstrates that privacy concerns about learning analytics can be mitigated by requiring informed consent from participants, establishing protocols for the collection and management of personally identifiable information, and advocating privacy rights of patrons. By synthesizing and expanding on viewpoints from the literature, this article offers recommendations pertaining to the collection, analysis, and management of patron data that are gathered for the purpose of learning analytics.

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