Using Data Mining for Predicting Relationships between Online Question Theme and Final Grade

As higher education diversifies its delivery modes, our ability to use the predictive and analytical power of educational data mining (EDM) to understand students\u27 learning experiences is a critical step forward. The adoption of EDM by higher education as an analytical and decision making tool is offering new opportunities to exploit the untapped data generated by various student information systems (SIS) and learning management systems (LMS). This paper describes a hybrid approach which uses EDM and regression analysis to analyse live video streaming (LVS) students\u27 online learning behaviours and their performance in their courses. Students\u27 participation and login frequency, as well as the number of chat messages and questions that they submit to their instructors, were analysed, along with students\u27 final grades. Results of the study show a considerable variability in students\u27 questions and chat messages. Unlike previous studies, this study suggests no correlation between students\u27 number of questions/chat messages/login times and students\u27 success. However, our case study reveals that combining EDM with traditional statistical analysis provides a strong and coherent analytical framework capable of enabling a deeper and richer understanding of students\u27 learning behaviours and experiences

Utilizing Big Data Analytics to Improve Education

Analytics can be defined as the process of determining, assessing, and interpreting meaning from volumes of data. It has been categorized in three different categories - descriptive, predictive and prescriptive. Predictive analysis can serve many segments of society as it can reveal hidden relationship which may not be apparent with descriptive modeling. Analytics advancement plays an important role in higher education planning. It answers several questions such as -which students will enroll in particular course, what courses are on trending or obsolete, what is the level of student satisfaction in the current education system, effectiveness of online study environment, how to design a better curriculum, likelihood of students transfer, drop out or failure to complete the course. Not only, data analytics helps in analyzing above points but also can be helpful in predictive modeling for faculty, administrative and students groups who are looking out for genuine results about the university rankings, based on which they make their decisions. Using the dataset “Academic Ranking of World Universities, 2003-2014”, we studied and analyzed to forecast how university’s management and faculty could adapt to changes to improve their education and thereby the ranking of their universities in the upcoming years. Microsoft SQL Server Data Mining Add-ins Excel 2008 was employed as a software mining tool for predicting the trending university ranking. This research paper concentrates upon predictive analysis of university ranking using forecasting based on data mining technique

Developing and Applying Smartphone Apps in Online Courses

Online courses provide students flexible access to class at anytime and anywhere. Most online courses currently rely on computer-based delivery. However, computers still burden instructors and students with limited mobility and flexibility. To provide more convenient access to online courses, smartphones have been increasingly adopted as a mobile method to access online courses. In this paper, we share our practical experience in designing and developing a smartphone platform for accessing online courses. The main contributions of this paper include: 1) we present the main technical issues of applying smartphones to online courses; 2) we discuss several key factors of designing, developing and delivering online courses that support smartphone access

An Analysis of User-Generated Comments on the Development of Social Mobile Learning

In this study, the authors used a mixed-method approach to analyze user-generated comments on social mobile learning from three leading news sites that report the latest development in higher education. Koole’s mobile learning model was used to code comments made by the public on the three news sites. Results showed that social mobile learning has gained an increasing public engagement in the past four years. Responders’ discussion in the comments primarily focused on four themes of social mobile learning: technology adoption, effective design, faculty training, and student training. In the end, the authors discussed the implications for developers and educators and concluded with recommendations for future research in social mobile learning using user-generated comments

Crowdsourcing Management Education Assessment

Assessment is now at the center of the new business education zeitgeist. This focus is the direct result of feedback from the business community regarding the growing gap between their needs and graduates from many business schools. Recently this divide has fallen under even closer scrutiny because of increasing student debt and the growing controversy over return-on- investment. Today business leaders are looking for web-savvy, problem-solving graduates. To this end, AACSB and regional accrediting bodies are calling for the adoption of comprehensive collaborative learning strategies to better align graduates’ skill sets with the real needs of business. Crowdsourcing, which is the process of connecting students and faculty with a broad- based group of both internal and external resources, is receiving increased attention throughout the assessment community. Within this context, crowdsourcing broadens the resource pool and thus provides for improved quality assurance in terms of meaning, quality, integrity, accountability and transparency. The proposed crowdsourcing-based quality assurance strategy is illustrated using sample data from a recent MBA program assessment. This article also outlines how the crowdsourcing can be used to enhance student learning outcomes via specific implementation strategies

The Role of Delivery Methods on the Perceived Learning Performance and Satisfaction of IT Students in Software Programming Courses

More and more information technology (IT) programs are offering distance learning courses to their students. However, to date, there are a very limited number of published articles in the IT education literature that compare how different methods of delivering distance course relate to undergraduate students’ learning outcomes in IT software programming courses taught by the same instructor. Thus, we conducted a case study to assess the predictive relationships between distance course delivery method (face-to-face, satellite broadcasting, and live video-streaming) and students’ perceived learning performance and satisfaction in IT software programming courses taught by the same instructor. The results suggested that the choice of delivery method was related to students’ satisfaction and programming skill enhancement. However, we did not find a relationship between the delivery method and the students’ perceived learning performance. Specifically, the participants in the face-to-face delivery method group were more likely to feel satisfied with the delivery method than the students using the other two delivery methods (i.e., satellite broadcasting and live video streaming)

UTILIZAÇÃO DE ALGORITMOS DE AGRUPAMENTO NA MINERAÇÃO DE DADOS EDUCACIONAIS

A mineração de dados educacionais é uma tarefa importante dado o grande volume de informações produzidas pelos softwares educacionais. Minerar significa buscar padrões relevantes que possam ser usados para aprimorar os processos de aprendizagem. Este estudo analisa ferramentas de mineração em três conjuntos de dados públicos: Geometry, Chinese Tone Study e Álgebra I 2006. Os resultados obtidos foram tabulados e analisados através dos critérios de homogeneidade e separação. A análise identificou que as ferramentas são insuficientes para trabalhar com dados educacionais. Contudo, a ferramenta imunológica foi a que apresentou melhores resultados

Evaluating the Data Analytic Features of Blackboard Learn 9.1

Learning Management Systems (LMSs) track and store vast quantities of data on student engagement with course content. Research shows that Higher Education Institutes can harness the power of this data to build a better understanding of student learning. This study is an exploratory Learning Analytics initiative to evaluate the inbuilt analytic features available within Blackboards LMS solution namely Blackboard Learn 9.1 to determine if it informs academic staff on student engagement. The two analytic features analysed in this study are Module Reports and Blackboard’s inbuilt early warning system called the “Retention Center”. Analysis of LMS variables extracted from these analytic features established a statistically significant weakly positive correlation between hit activity, login activity and student examination results. A statistically significant weakly positive correlation was also established between Multiple Choice Quiz (MQQ) score and examination results. These findings suggest that activity within LMS, measured by logins, hit activity and results in MCQs provide indicators of student academic performance. Lecturers involved in the study felt the analytic features provided them with a sense of student engagement with course modules and better understanding of their student cohorts

Evaluating the Data Analytic Features of Blackboard Learn 9.1

Learning Management Systems (LMSs) track and store vast quantities of data on student engagement with course content. Research shows that Higher Education Institutes can harness the power of this data to build a better understanding of student learning. This study is an exploratory Learning Analytics initiative to evaluate the inbuilt analytic features available within Blackboards LMS solution namely Blackboard Learn 9.1 to determine if it informs academic staff on student engagement. The two analytic features analysed in this study are Module Reports and Blackboard’s inbuilt early warning system called the “Retention Center”. Analysis of LMS variables extracted from these analytic features established a statistically significant weakly positive correlation between hit activity, login activity and student examination results. A statistically significant weakly positive correlation was also established between Multiple Choice Quiz (MQQ) score and examination results. These findings suggest that activity within LMS, measured by logins, hit activity and results in MCQs provide indicators of student academic performance. Lecturers involved in the study felt the analytic features provided them with a sense of student engagement with course modules and better understanding of their student cohorts

Learning analytics and educational data mining in practice: a systematic literature review of empirical evidence.

ABSTRACT This paper aims to provide the reader with a comprehensive background for understanding current knowledge on Learning Analytics (LA) and Educational Data Mining (EDM) and its impact on adaptive learning. It constitutes an overview of empirical evidence behind key objectives of the potential adoption of LA/EDM in generic educational strategic planning. We examined the literature on experimental case studies conducted in the domain during the past six years (2008)(2009)(2010)(2011)(2012)(2013). Search terms identified 209 mature pieces of research work, but inclusion criteria limited the key studies to 40. We analyzed the research questions, methodology and findings of these published papers and categorized them accordingly. We used non-statistical methods to evaluate and interpret findings of the collected studies. The results have highlighted four distinct major directions of the LA/EDM empirical research. We discuss on the emerged added value of LA/EDM research and highlight the significance of further implications. Finally, we set our thoughts on possible uncharted key questions to investigate both from pedagogical and technical considerations