Data-Driven Analysis of Engagement in Gamified Learning Environments: A Methodology for Real-Time Measurement of MOOCs

Welfare and economic development is directly dependent on the availability of highly skilled and educated individuals in society. In the UK, higher education is accessed by a large percentage of high school graduates (50% in 2017). Still, in Brazil, a limited number of pupils leaving high schools continue their education (up to 20%). Initial pioneering efforts of universities and companies to support pupils from underprivileged backgrounds, to be able to succeed in being accepted by universities include personalised learning solutions. However, initial findings show that typical distance learning problems occur with the pupil population: isolation, demotivation, and lack of engagement. Thus, researchers and companies proposed gamification. However, gamification design is traditionally exclusively based on theory-driven approaches and usually ignore the data itself. This paper takes a different approach, presenting a large-scale study that analysed, statistically and via machine learning (deep and shallow), the first batch of students trained with a Brazilian gamified intelligent learning software (called CamaleOn), to establish, via a grassroots method based on learning analytics, how gamification elements impact on student engagement. The exercise results in a novel proposal for real-time measurement on Massive Open Online Courses (MOOCs), potentially leading to iterative improvements of student support. It also specifically analyses the engagement patterns of an underserved community

A Systematic Review of the Factors that Impact the Prediction of Retention and Dropout in Higher Education

Identifying factors that affect academic dropout and retention is a research area that brings a plurality of opinions and concepts. This article identifies current primary studies to understand the main factors related to dropout and retention. It is quantitative, exploratory, and explanatory research of an applied nature, using the technical procedures of case study and bibliographic research. The systematic review of the literature identifies the factors that impact academic dropout and retention and serves as a basis for a machine learning project. Academic, demographic, and learning factors can predict dropouts and retention. The definition of the factors used and the way of use is essential to obtain good forecasting results. The identified factors were used in the institution

The efficacy of learning analytics interventions in higher education: A systematic review

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this record.Educational institutions are increasingly turning to learning analytics to identify and intervene with students at risk of underperformance or discontinuation. However, the extent to which the current evidence base supports this investment is currently unclear, and particularly so in relation to the effectiveness of interventions based on predictive models. The aim of the present paper was to conduct a systematic review and quality assessment of studies on the use of learning analytics in higher education, focusing specifically on intervention studies. Search terms identified 689 articles, but only 11 studies evaluated the effectiveness of interventions based on learning analytics. These studies highlighted the potential of such interventions, but the general quality of the research was moderate, and left several important questions unanswered. The key recommendation based on this review is that more research into the implementation and evaluation of scientifically driven learning analytics is needed to build a solid evidence base for the feasibility, effectiveness, and generalizability of such interventions. This is particularly relevant when considering the increasing tendency of educational institutions around the world to implement learning analytics interventions with only little evidence of their effectiveness.The research reported in this paper was supported by the University of Exeter’s Effective Learning Analytics project

Addressing the education puzzle : the distribution of education and economic reform

No country has achieved sustained economic development without substantially investing in human capital. Previous studies have shown the handsome returns to various forms of basic education, research, training, learning-by-doing, and capacity-building. But education by itself does not guarantee successful development, as history has shown in the former Soviet bloc, Sri Lanka, the Philippines, and the Indian states of Kerala and West Bengal. The question is, when and how does education bring high payoffs? Although theory has suggested a strong causal link between education and growth, the empirical evidence has not been unanimous and conclusive. The authors examine two explanatory factors. First, who gets educated matters a good deal, but the distribution of education is complex and not much has been written about it. They construct an asset allocation model that elucidates the importance of the distribution of education to economic development. Second, how education affects growth is greatly affected by the economic policy environment. Policies determine what people can do with their education. Reform of trade, investment, and labor policies can increase the returns from education. Using panel data from 12 Asian and Latin American countries for 1970-94, they investigate the relationship between education, policy reform, and economic growth. Their empirical results are promising. First, the distribution of education matters. Unequal distribution of education tends to have a negative impact on per capita income in most countries. Moreover, controlling for human capital distribution and the use of appropriate functional form specifications consistent with the asset allocation model makes a difference for the effect of average schooling on per capita income. Controlling for education distribution leads to positive and significant effects of average schooling on per capita income, while failure to do so leads to insignificant, even negative effects, of average education. Second, the policy environment matters a great deal. Our results indicate that economic policies that suppress market forces tend to dramatically reduce the impact of human capital on economic growth. Investment in human capital can have little impact on growth unless people can use education in competitive and open markets. The larger and more competitive these markets are, the greater are the prospects for using education and skills.Curriculum&Instruction,Economic Theory&Research,Decentralization,Public Health Promotion,Health Monitoring&Evaluation,Health Monitoring&Evaluation,Teaching and Learning,Curriculum&Instruction,Economic Theory&Research,Gender and Education

Using Prediction ML algorithm for predicting early Student Attrition in Higher Education

This research aims at using predictive models that enable us to predict students who are at risk of dropping out and identify the factors that possibly lead to this dropout. Through the results obtained, concerned stakeholders will be able to effectively develop strategies and initiatives to help decrease the percentage of students’ attrition. There are different reasons why students drop from their courses which could be related to academic issues or personal issues that stop them from being active students. Due to these many reasons of students dropping out, universities are impacted negatively in terms of the financial costs as they lose an amount of money from those students, and sometimes they lose the funds from public sponsors to major activities in universities. The proposal aims at exploring the various reasons that influence students’ decision to withdraw and what will be the best model for the prediction. I will use data from the open-source Kaggle and use Python to explore and preprocess the data. I will also use Tableau for getting visual insights from the available dataset

Towards learning analytics adoption: A mixed methods study of data-related practices and policies in Latin American universities

In Latin American universities, Learning Analytics (LA) has been perceived as a promising opportunity to leverage data to meet the needs of a diverse student cohort. Although universities have been collecting educational data for years, the adoption of LA in this region is still limited due to the lack of expertise and policies for processing and using educational data. In order to get a better picture of how existing data‐related practices and policies might affect the incorporation of LA in Latin American institutions, we conducted a mixed methods study in four Latin American universities (two Chilean and two Ecuadorian). In this paper, the qualitative data were based on 37 interviews with managers and 16 focus groups with 51 teaching staff and 45 students; the quantitative data were collected through two surveys answered by 1884 students and 368 teachers, respectively. The findings reveal opportunities to incorporate LA services into existing data practices in the four case studies. However, the lack of reliable information systems and policies to regulate the use of data imposes challenges that need to be overcome for future LA adoption.In Latin American universities, Learning Analytics (LA) has been perceived as a promising opportunity to leverage data to meet the needs of a diverse student cohort. Although universities have been collecting educational data for years, the adoption of LA in this region is still limited due to the lack of expertise and policies for processing and using educational data. In order to get a better picture of how existing data‐related practices and policies might affect the incorporation of LA in Latin American institutions, we conducted a mixed methods study in four Latin American universities (two Chilean and two Ecuadorian). In this paper, the qualitative data were based on 37 interviews with managers and 16 focus groups with 51 teaching staff and 45 students; the quantitative data were collected through two surveys answered by 1884 students and 368 teachers, respectively. The findings reveal opportunities to incorporate LA services into existing data practices in the four case studies. However, the lack of reliable information systems and policies to regulate the use of data imposes challenges that need to be overcome for future LA adoption

Dropout causes of students funded by the National Student Financial Aid Scheme in South African universities

The dropout of students funded by the National Student Financial Aid Scheme (NSFAS) is a perennial problem in many higher education institutions (HEIs) in South Africa. Despite this, little research has been conducted to investigate this phenomenon, and this study sought to address this gap by investigating the dropout of NSFAS-funded students from HEIs in Northern Gauteng. The study adopted a qualitative methodology and a phenomenological design to explore the lived experiences of students who dropped out of HEIs. Thirty-one NSFAS-funded students, three senior management officials from three HEIs and one NSFAS senior official were purposively selected to form part of the study. Semi-structured interviews, document analysis and observations were utilised as reseach instruments and interpretative phenomenological analysis (IPA) was employed to analyse data. The findings of the study established that a lack of support for students, and personal, socioeconomic, institutional and health factors contributed to the dropout of students from HEIs. It was further established that the majority of students who dropped out did so because of the inefficient operations of NSFAS and the new student-centred model. The study also found that insufficient funding, late allocation of funds, stringent NSFAS requirements, lack of communication, late payment or nonpayment of allowances contributed to students’ dropout. To address these shortfalls, the study recommends that the student-centred model should be overhauled and replaced with an integrated system including departments such as DOH, SARS, DSD and DOL to identify students who are eligible for funding and assist in the efficient administration of NSFAS. It is further recommended that funding administered by both the national and provincial government departments be centralized and administred by the NSFAS to circumvent double dipping. Finally, it is recommended that students who fall within the R0 – R350,000 per annum household income category including SASSA beneficiaries be flagged by the system to automatically qualify for funding.Educational Management and LeadershipD. Ed. (Education Management

Learning analytics for the global south

Learning Analytics for the Global South is a compilation of papers commissioned for the Digital Learning for Development (DL4D) project. DL4D is part of the Information Networks in Asia and Sub-Saharan Africa (INASSA) program funded jointly by the International Development Research Centre (IDRC) of Canada and the Department for International Development (DFID) of the United Kingdom, and administered by the Foundation for Information Technology Education and Development (FIT-ED) of the Philippines. DL4D aims to examine how digital learning could be used to address issues of equity, quality, and efficiency at all educational levels in developing countries. Over the past two years, DL4D has brought together leading international and regional scholars and practitioners to critically assess the potentials, prospects, challenges, and future directions for the Global South in key areas of interest around digital learning. It commissioned discussion papers for each of these areas from leading experts in the field: Diana Laurillard of the University College London Knowledge Lab, for learning at scale; Chris Dede of Harvard University, for digital game-based learning; Charalambos Vrasidas of the Centre for the Advancement of Research and Development in Educational Technology, for cost-effective digital learning innovations; and for learning analytics, the subject of this compilation, Dragan Gašević of the University of Edinburgh Moray House School of Education and School of Informatics. Each discussion paper is complemented by responses from a developing country-perspective by regional experts in Asia, Latin America, Africa, and the Middle East. Learning Analytics for the Global South considers how the collection, analysis, and use of data about learners and their contexts have the potential to broaden access to quality education and improve the efficiency of educational processes and systems in developing countries around the world. In his discussion paper, Prof. Gašević articulates these potentials and suggests how learning analytics could support critical digital learning and education imperatives such as quality learning at scale and the acquisition of 21st century skills. Experts from Africa (Paul Prinsloo of the University of South Africa), Mainland China (Bodong Chen of the University of Minnesota, USA and Yizhou Fan of Peking University, People’s Republic of China), Southeast Asia (Ma. Mercedes T. Rodrigo of the Ateneo de Manila University, Philippines), and Latin America (Cristóbal Cobo and Cecilia Aguerrebere, both of the Ceibal Foundation, Uruguay) situate Prof. Gašević’s proposals in their respective regional contexts, framing their responses around six key questions: 1. What are the main trends and challenges in education in your region? 2. How can learning analytics address these challenges? 3. What models of learning analytics adoption would be most effective in your region? 4. What are the barriers in adoption of learning analytics in your region and how could these be mitigated? 5. How do you envision ethical use and privacy protection in connection with learning analytics being addressed in your region? 6. How can the operationalization of learning analytics be futureproofed in your region? We hope that this compilation will serve as a springboard for deeper conversations about the adoption and sustained use of learning analytics in developing countries – its potential benefits and risks for learners, educators, and educations systems, as well as the ways to move forward that are rigorous, context-appropriate, ethical, and accountable.This work was created with financial support from the UK Government’s Department for International Development and the International Development Research Centre, Canada. The views expressed in this work are those of the authors and do not necessarily represent those of the UK Government’s Department for International Development; the International Development Research Centre, Canada or its Board of Governors; the Foundation for Information Technology Education and Development; or the editors

Virtual learning environments in the light of mind maps and flashcards : a systematic literature review

Mind Maps' and Flashcards' techniques are widely used in various sectors for different purposes. The use of Mind Maps focuses on the elaboration of physical models representing the neural connections between certain subjects, as well as for projects organization, for educational purposes or as a way of presenting content. Flashcards, on the other hand, propose a learning methodology based on key points of the approached content, commonly used for Foreign Language teaching and learning. Thus, this Systematic Literature Review proposes a theoretical and exploratory research on the application of Mind Maps and Flashcards in Virtual Learning Environments (VLE). Through the analyzed bibliography, approaches which connect Foreign Language teaching and learning with Flashcards were found and the studies proved this association to be feasible. However, neither VLE based on Mind Maps or Flashcards were found, nor theoretical evidence describing the implementation of both methodologies together

Brazilian Higher Education Analysis Through Knowledge Discovery: Annual and Temporal Approaches

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Survey Methodologies and Marketing ResearchThis project presents the Ph.D. thesis proposal in the Information Management area and aims to contextualize the scenario of Higher Education Institutions (HEIs) in Brazil, generate new knowledge and provide subsidies to justify the relevance of the problem investigated and its contributions. It explores the Brazilian Higher Education Census, from 2010 to 2015, and other official and public databases in order to generate new knowledge, based on the fact that knowledge is the main factor of social development in the Age of the Knowledge Society and Economy. It proposes to answer the following research question: "How does the annual and temporal analysis of the Brazilian Higher Education Census and other public and official databases generate new knowledge and provide strategic information to ensure the Higher Education Institutions mission’s accomplishment?" To achieve its objective, it adopts an inductive research process as a research strategy, divided into two phases: an exploratory study, followed by the knowledge generation phase. It is an interpretative, constructionist, and quantitative study. As a methodological resource, it uses the Self-Organizing Maps (SOM), a type of neural network that explores hidden patterns in a large volume of data. In this case, specifically, it is used to discover new knowledge in the area of higher education, considering the higher education institutions, their undergraduate courses, teachers, and students. Besides, and therefore, it assesses the internal dynamics of the higher education institutions and, according to the Resource-Based View (RBV) theory, presents a new approach to identify their internal resources - a gap in the current literature. The proposed approach contributes to fostering new forms of relationship, based on the combination of similar or complementary resources between and among the institutions, which will enable them to become more entrepreneurial and to behave more collaboratively. The research also contributes to 1) the adoption of an innovative methodology - SOM - for the area of Education, specifically Higher Education and a new typology for grouping the educational institutions, courses, teachers and students; 2) the advancement of the theory of RBV; 3) the area of Education, lacking quantitative studies; and 4) the extension of the concept of the entrepreneurial university – the enhanced triple helices, based on their complementary and similar resources. This new knowledge plays a significant role in the implementation of competitive responses or decisions to take in a fiercely competitive environment and contributes to the advancement of the theory under study. Keywords: knowledge discovery, higher education, Self-Organizing Maps - SOM, entrepreneurial university.Este projeto apresenta a proposta de tese de doutoramento na área de Gestão da Informação e tem como objetivo contextualizar o cenário das Instituições de Ensino Superior (IESs) do Brasil, gerar novos conhecimentos e fornecer subsídios para justificar a relevância do problema investigado e suas contribuições. Explora o Censo Brasileiro do Ensino Superior, de 2010 a 2015, e outros bancos de dados oficiais e públicos, com o intuito de gerar novos conhecimentos, pautando-se no fato de que o conhecimento é o principal fator de desenvolvimento, tanto social quanto econômico, na Era da Economia e da Sociedade do Conhecimento. Sendo assim, se propõe a responder à seguinte pergunta de investigação: "Como a análise anual e temporal do Censo Brasileiro de Ensino Superior (IES) e de outros bancos de dados oficiais e públicos geram novos conhecimentos e fornecem informações estratégicas para garantir o cumprimento da missão central das Instituições de Ensino Superior? " Para alcançar o seu objetivo, adota um processo de investigação indutivo como estratégia de pesquisa, dividido em duas fases: um estudo exploratório, seguido da fase de geração de conhecimento. Trata-se de um estudo interpretativo, construcionista e quantitativo. Como recurso metodológico utiliza os Self-Organizing Maps (SOM), um tipo de rede neural que lida com um grande volume de dados para explorar padrões ocultos. Neste caso, especificamente, com o intuito de descobrir novos conhecimentos na área da educação superior, em especial, nas instituições de ensino, seus cursos de graduação, professores e estudantes. Além disso, e como consequência, avalia a dinâmica interna das instituições de ensino estudadas e, de acordo com a teoria da Visão Baseada em Recursos (RBV), apresenta uma nova abordagem para se avaliar os recursos internos institucionais - uma lacuna na literatura atual. Contribui também para fomentar novas formas de relacionamento, baseadas na combinação de recursos similares ou complementares entre as próprias instituições, o que lhes permitirá tornarem-se mais empreendedoras e comportarem-se de forma mais colaborativa. Como contributos, pode-se assinalar: 1) a adoção de uma metodologia inovadora – os SOM – para a área da Educação, especificamente, da Educação Superior e uma nova tipologia para o agrupamento das instituições de ensino, cursos de graduação, professores e alunos; 2) sua contribuição para o avanço da teoria da RBV, com a proposição de uma nova abordagem para a identificação e a análise dos recursos internos institucionais; 3) a contribuição para a área da Educação, carente de estudos de natureza quantitativa; e 4) a proposição de ampliação do conceito da tripa hélice para um formato aprimorado, com base em seus recursos complementares e similares. Esse novo conhecimento desempenha um papel significativo na implementação de respostas ou decisões competitivas a serem tomadas, em um ambiente competitivo acirrado, além de contribuir para o avanço das teorias em estudo