Understanding the Impact of Motivation on the Effectiveness of Various Content Delivery Methods in Training Program Development: A Mixed-Methods Evaluation

The purpose of this study was to evaluate an online training program designed for part-time undergraduate Desk Assistants (DAs) employed by Louisiana State University’s (LSU) department of Residence Education. The evaluation of the training program included a comparison of video and lecture versions of a training program with comparable content to determine the effectiveness across a set of four outcomes: motivation during training, motivation after training, satisfaction, and learning. Additionally, this research contributed to the understanding of the impact of technology-mediated learning in training by examining factors that may differentially benefit or challenge the effectiveness of the training delivery method. Specifically, learner characteristics and motivation to learn were measured as antecedents. Data collection included both quantitative and qualitative methods. Quantitative analyses focused on changes in knowledge and motivation as a result of delivery method, as well as the impact of learner characteristics on overall training effectiveness. Knowledge tests and self-report scales were used to collect quantitative information. Qualitative data was collected via survey, discussion, and behavior observation, then analyzed for themes that help to more fully clarify the role of motivation by providing data regarding the factors that benefit or challenge trainees as they go through the training program. Results suggest an advantage for video training over lecture. However, the overall effectiveness of the training program was influenced by both learner characteristics and motivation. Although new employees showed learning gains regardless of motivation, learning was correlated with motivation for returning employees, such that those with higher motivation scores demonstrated knowledge gains, whereas returning employees with poor motivation did not. Implications and interventions for improving future training based on study results are discussed

A framework for strategic planning of data analytics in the educational sector

The field of big data and data analysis is not a new one. Big data systems have been investigated with respect to the volume of the data and how it is stored, the data velocity and how it is subject to change, variety of data to be analysed and data veracity referring to integrity and quality. Higher Education Institutions (HEIs) have a significant range of data sources across their operations and increasingly invest in collecting, analysing and reporting on their data in order to improve their efficiency. Data analytics and Business Intelligence (BI) are two terms that are increasingly popular over the past few years in the relevant literature with emphasis on their impact in the education sector. There is a significant volume of literature discussing the benefits of data analytics in higher education and even more papers discussing specific case studies of institutions resorting on BI by deploying various data analytics practices. Nevertheless, there is a lack of an integrated framework that supports HEIs in using learning analytics both at strategic and operational level. This research study was driven by the need to offer a point of reference for universities wishing to make good use of the plethora of data they can access. Increasingly institutions need to become ‘smart universities’ by supporting their decisions with findings from the analysis of their operations. The Business Intelligence strategies of many universities seems to focus mostly on identifying how to collect data but fail to address the most important issue that is how to analyse the data, what to do with the findings and how to create the means for a scalable use of learning analytics at institutional level. The scope of this research is to investigate the different factors that affect the successful deployment of data analytics in educational contexts focusing both on strategic and operational aspects of academia. The research study attempts to identify those elements necessary for introducing data analytics practices across an institution. The main contribution of the research is a framework that models the data collection, analysis and visualisation in higher education. The specific contribution to the field comes in the form of generic guidelines for strategic planning of HEI data analytics projects, combined with specific guidelines for staff involved in the deployment of data analytics to support certain institutional operations. The research is based on a mixed method approach that combines grounded theory in the form of extensive literature review, state-of-the-art investigation and case study analysis, as well as a combination of qualitative and quantitative data collection. The study commences with an extensive literature review that identifies the key factors affecting the use of learning analytics. Then the research collected more information from an analysis of a wide range of case studies showing how learning analytics are used across HEIs. The primary data collection concluded with a series of focus groups and interviews assessing the role of learning analytics in universities. Next, the research focused on a synthesis of guidelines for using learning analytics both at strategic and operational levels, leading to the production of generic and specific guidelines intended for different university stakeholders. The proposed framework was revised twice to create an integrated point of reference for HEIs that offers support across institutions in scalable and applicable way that can accommodate the varying needs met at different HEIs. The proposed framework was evaluated by the same participants in the earlier focus groups and interviews, providing a qualitative approach in evaluating the contributions made during this research study. The research resulted in the creation of an integrated framework that offers HEIs a reference for setting up a learning analytics strategy, adapting institutional policies and revising operations across faculties and departments. The proposed C.A.V. framework consists of three phases including Collect, Analysis and Visualisation. The framework determines the key features of data sources and resulting dashboards but also a list of functions for the data collection, analysis and visualisation stages. At strategic level, the C.A.V. framework enables institutions to assess their learning analytics maturity, determine the learning analytics stages that they are involved in, identify the different learning analytics themes and use a checklist as a reference point for their learning analytics deployment. Finally, the framework ensures that institutional operations can become more effective by determining how learning analytics provide added value across different operations, while assessing the impact of learning analytics on stakeholders. The framework also supports the adoption of learning analytics processes, the planning of dashboard contents and identifying factors affecting the implementation of learning analytics

Student progress monitoring: teachers\u27 perceptions

The Mississippi Student Progress Monitoring System (MSPMS) was developed for the Mississippi Department of Education to be used to monitor student progress on the state framework which constitutes the curriculum for each course taught in Mississippi schools. This study was designed to investigate teachers’ perceptions of the implementation and use of the MSPMS. Research question 1 was to determine if the various independent variables of age, level of education, years of experience as an educator, level of school where teaching, perceived level of computer and/or technology comfort, perceived level of computer and/or technology experience, subject area taught, number of MSPMS tests created, number of MSPMS tests given, amount of support provided, whether program works, and importance of information gained from MSPMS made any difference in teachers’ perceptions of the implementation and use of the MSPMS; and research question 2 was to determine whether the teachers’ perceptions and the various independent variables had any significant relationships. Research question 3 looked at teachers’ attitudes toward MSPMS. There were no statistically significant differences among the dependent and independent variables. Findings for research question 2 showed that there were no statistically significant correlations among the dependent and independent variables. However, correlations among the independent variables revealed statistically significant relationships between age and years of experience, subjects taught and school level taught, technology experience and level of education, and subjects taught and number of tests given. Examination of the response frequencies for situations in the vignettes for research question 3 revealed that teachers reported feeling more frustrated than anything else when confronted with adversities with the technologies or the MSPMS. All of the findings in this study are limited to a rural Mississippi school district using MSPMS

“We’re being tracked at all times”: Student perspectives of their privacy in relation to learning analytics in higher education

Higher education institutions are continuing to develop their capacity for learning analytics (LA), which is a sociotechnical data mining and analytic practice. Institutions rarely inform their students about LA practices and there exist significant privacy concerns. Without a clear student voice in the design of LA, institutions put themselves in an ethical grey area. To help fill this gap in practice and add to the growing literature on students’ privacy perspectives, this study reports findings from over 100 interviews with undergraduate students at eight United States highereducation institutions. Findings demonstrate that students lacked awareness of educational data mining and analytic practices, as well as the data on which they rely. Students see potential in LA, but they presented nuanced arguments about when and with whom data should be shared; they also expressed why informed consent was valuable and necessary. The study uncovered perspectives on institutional trust that were heretofore unknown, as well as what actions might violate that trust. Institutions must balance their desire to implement LA with their obligation to educate students about their analytic practices and treat them as partners in the design of analytic strategies reliant on student data in order to protect their intellectual privacy

IQP Strong Authentication

The threat of online personal information breaches rises as people put more critical data online, and despite ample availability, strong authentication protecting this information is not being adopted quickly enough to address the threat. To better understand this problem, the IQP team designed and conducted a study to isolate factors leading to such behavior. The team found that people had trouble surmounting the shift to stronger tools, but once past that, they readily settled into permanent use. Also, personal connection to threats was correlated to a good impression of strong authentication. The solution may be online security education that induces a personal connection to the threat, so as to create a better incentive to overcome the obstacles of transitioning and increase security

Supporting student experience management with learning analytics in the UK higher education sector

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyWhile some UK Higher Education Institutes (HEIs) are very successful at harnessing the benefits of Learning Analytics, many others are not actually engaged in making effective use of it. There is a knowledge gap concerning understanding how Learning Analytics is being used and what the impacts are in UK HEIs. This study addresses this gap. More specifically, this study attempts to understand the challenges in utilising data effectively for student experience management (SEM) in the era of Big Data and Learning Analytics; to examine how Learning Analytics is being used for SEM; to identify the key factors affecting the use and impact of Learning Analytics; and to provide a systematic overview on the use and impact of Learning Analytics on SEM in HEIs by developing a conceptual framework. To achieve the research objectives, a qualitative research method is used. The data collection process firstly involves an exploratory case study in a UK university to gain a preliminary insight into the current status on the use of Big Data and Learning Analytics and their impact, and to determine the main focuses for the main study. The research then undertakes an extensive main study involving 30 semi-structured interviews with participants in different UK universities to develop more in-depth knowledge and to present systematically the key findings using a theoretical framework underpinned by relevant theories. Based on the evidence collected from the exploratory case study and interviews, the study identifies the key challenges in utilising data and Learning Analytics in the era of Big Data. These include issues related to data quality, data consistency, data reliability, data analysis, data integration, data and information overload, lack of data, information availability and problems with systems. A series of critical factors affecting the use of Learning Analytics is emerged and mapped out from a technology-organisation-environment-people (TOE+P) perspective. The technology-related factors include Usability, Affordability, Complexity and System integration. The organisation-related factors cover Resource, Data Driven Culture, Senior management support and Strategic IT alignment. The environment-related factors include Competitive pressure, Regulatory environment and External support. Most importantly, the findings emphasise the importance of the people-related factor in addition to TOE factors. The people-related factors include People’s engagement with using data and Learning Analytics, People’s awareness of Data Protection and Privacy and Digital Literacy. The impacts of the Learning Analytics are also identified and analysed using organisational absorptive capacity theory. The findings are integrated in the final theoretical framework and demonstrate that the HEIs’ capabilities in terms of data acquisition, assimilation, transformation and exploitation supported by Learning Analytics enable them to improve student experience management. This study makes new contributions to research and theory by providing a theoretical framework on understanding the use and impact of Learning Analytics in UK HEIs. It also makes important practical contributions by offering valuable guidelines to HEI managers and policy makers on understanding the value of Learning Analytics and know how to maximise the impact of Big Data and Learning Analytics in their organisations

The Use of Dining Data to Increase Retention and Academic Success in Residential First-Year Students

Higher education leaders have been conducting research over the last 50 years to pinpoint why students enroll in college and then end up leaving. Research shows that there is not a single factor that influences a student’s decision, but it is a variety of factors. Influential factors include class attendance, a sense of belonging, motivation, academic rigor and performance, finances, and more. A student’s physical wellness and mental state can also impact their academic success and life while in college. First-year students often experience depression, anxiety, and loneliness as they try to successfully transition to college. Most of these influential factors are quantified and measured by institutions in real-time through predictive analytics to identify students at risk of leaving. One data point that has not been thoroughly researched is dining data. This non-experimental, causal-comparison study investigated the relationship between dining data and academic success and retention. Analysis of the data showed that dining data can predict academic success and retention, however, the strongest correlation existed between a significant change in dining habits predicting persistence into the next semester. The findings indicate that dining data should be collected by institutions and integrated into predictive analytics to identify at-risk students. Further research should be conducted to generalize the use of dining data in predictive analytics as well as investigate how dining data can be paired with other data points to further identify students in need of assistance

Open to Exploitation: America\u27s Shoppers Online and Offline

Most Americans who use the Internet have little idea how vulnerable they are to abuse by online and offline marketers and how the information they provide can be used to exploit them. That is one conclusion from this unprecedented national phone survey conducted by the Annenberg Public Policy Center. The study indicates that many adults who use the internet believe incorrectly that laws prevent online and offline stores from selling their personal information. They also incorrectly believe that stores cannot charge them different prices based on what they know about them. Most other internet-using adults admit that they simply don’t know whether or not laws protect them. The survey further reveals that the majority of adults who use the internet do not know where to turn for help if their personal information is used illegally online or offline. The study\u27s findings suggest a complex mix of ignorance and knowledge, fear and bravado, realism and idealism that leaves most internet-using adult American shoppers open to financial exploitation by retailers