Does Lecture Capturing Impact Student Performance and Attendance in an Introductory Accounting Course?

The study empirically examines the interplay between lecture capturing viewership, performance and attendance for students in the Middle Eastern country of Qatar. The sample consists of 254 students enrolled in an introductory accounting class either in the Fall semester or in the Spring semester. We show a weak positive relationship between lecture capturing and performance, especially in the presence of other variables such as GPA, attendance, gender and seniority. However, we do not find that lecture capturing reduces attendance. Actual performance results are contrasted with students' perception of the usefulness and effectiveness of lecture capturing. Survey responses reveal that, overall, students attribute a great deal of credit to this pedagogical resource. They stated that lecture capturing clarifies concepts discussed in class, assists in studying for exams, enhances exam results and increases interest in the course. However, the majority of low-performing students believe lecture capturing to be a substitute for attending traditional lectures.Scopu

Lecture recording in higher education: Risky business or evolving open practice

Reports on a survey into the copyright and intellectual property (IPR) policies of UK higher education institutions with regards to lecture recording. The practice of using institutional semi-automated lecture recording systems is becoming mainstream with 71% of institutions reporting using it in 2016 (UCISA, 2016). However, these systems raise a number of issues related to copyright and IPR that in some cases are documented in specific policy documents. Issues that arise include the consent that is obtained from academic staff, the ownership of the resulting outputs and responsibility and advice given for the use of third party content in the lectures. These issues are also often linked to, or conflated with wider ethical issues such as identity, privacy and academic freedom. The findings from the survey are presented alongside a policy analysis of IPR documents and policies from 11 institutions. These are compared to the guidance provided by Jisc (2015). The findings from the survey reveal that most institutions are still developing their IPR policy with regards to lecture recording, that many institutions seek consent from lecturers, but there is an increasing move towards making lecture recording opt-out as opposed to opt-in. The survey revealed in 94% of cases the lecturers or presenter is responsible for any third party content contained within their lecture and while institutions do offer advice about dealing with third party content, much of it is delivered in a relatively passive way, through agreeing to use the system or by information made available online in guides. The findings from the policy analysis suggest that those institutions with a high level of institutional control tend to have a higher level of comprehensiveness of approach towards lecture recording. Additionally the institutions that provide a higher level of support for copyright advice, have a tendency towards open practice and higher levels of appetite for risk. Good practice advice for institutions and recommendations for further research are presented as part of this study

Perceived Motivational Affordances: Capturing and Measuring Students' Sense-Making Around Visualizations of their Academic Achievement Information.

The efficacy of learning analytics is predicated on the validity of techniques used to uncover patterns about student learning and engagement, and the ways in which these patterns are communicated to various stakeholders. How students understand representations of their learning, and whether or not those representations motivate them in positive ways, is not well understood. This dissertation addresses this gap in the literature through two complementary studies. Study 1 utilizes qualitative interviews (n = 60) to investigate how students at-risk of college failure interpret visual representations of their potential academic achievement. Findings suggest an interplay between the information communicated by visualizations and students’ own inclinations towards the information they wished to see. Visualizations showing only the participants academic information, for example, evoked statements focused on personal growth from students when they interpreted the graphs. Visualizations that cast an individual student’s performance against the class average, however, evoked maladaptive responses. Study 2 designed and validated the Motivated Information-Seeking Questionnaire (MISQ) using a college student sample drawn from across the country (n = 551). The MISQ measures constructs that are parallel to mastery, performance-avoid, and performance approach goal orientations as theorized by Achievement Goal Theory. Confirmatory Factor Analysis (CFA) was used to internally validate the MISQ scales, resulting in validation of the performance-approach information-seeking (PAIS) and performance-avoid information-seeking (PVIS) dimensions. Results of external validation indicated that PVIS and PAIS were empirically distinguishable from performance-approach and performance-avoid achievement goal orientations. Multiple regression analysis supported the predictive power of PVIS and PAIS with regard to students’ emotional responses to certain types of visualizations and to what they attributed their success and/or failure, after controlling for relevant demographic characteristics. Taken together, these studies increase our knowledge of the various dimensions students use while interpreting visualizations, and uncovered tensions between what students want to see, versus what it might be more motivationally appropriate for them to see. Both studies suggest three maxims for the design and use of visualizations: 1) Never assume that more information is better; 2) Anticipate and mitigate against potential harm; and 3) Always suggest a way for students to grow.PhDEducation and PsychologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133441/1/aguilars_1.pd

Virtual learning process environment (VLPE): a BPM-based learning process management architecture

E-learning systems have significantly impacted the way that learning takes place within universities, particularly in providing self-learning support and flexibility of course delivery. Virtual Learning Environments help facilitate the management of educational courses for students, in particular by assisting course designers and thriving in the management of the learning itself. Current literature has shown that pedagogical modelling and learning process management facilitation are inadequate. In particular, quantitative information on the process of learning that is needed to perform real time or reflective monitoring and statistical analysis of students’ learning processes performance is deficient. Therefore, for a course designer, pedagogical evaluation and reform decisions can be difficult. This thesis presents an alternative e-learning systems architecture - Virtual Learning Process Environment (VLPE) - that uses the Business Process Management (BPM) conceptual framework to design an architecture that addresses the critical quantitative learning process information gaps associated with the conventional VLE frameworks. Within VLPE, course designers can model desired education pedagogies in the form of learning process workflows using an intuitive graphical flow diagram user-interface. Automated agents associated with BPM frameworks are employed to capture quantitative learning information from the learning process workflow. Consequently, course designers are able to monitor, analyse and re-evaluate in real time the effectiveness of their chosen pedagogy using live interactive learning process dashboards. Once a course delivery is complete the collated quantitative information can also be used to make major revisions to pedagogy design for the next iteration of the course. An additional contribution of this work is that this new architecture facilitates individual students in monitoring and analysing their own learning performances in comparison to their peers in a real time anonymous manner through a personal analytics learning process dashboard. A case scenario of the quantitative statistical analysis of a cohort of learners (10 participants in size) is presented. The analytical results of their learning processes, performances and progressions on a short Mathematics course over a five-week period are also presented in order to demonstrate that the proposed framework can significantly help to advance learning analytics and the visualisation of real time learning data

A data-assisted approach to supporting instructional interventions in technology enhanced learning environments

The design of intelligent learning environments requires significant up-front resources and expertise. These environments generally maintain complex and comprehensive knowledge bases describing pedagogical approaches, learner traits, and content models. This has limited the influence of these technologies in higher education, which instead largely uses learning content management systems in order to deliver non-classroom instruction to learners. This dissertation puts forth a data-assisted approach to embedding intelligence within learning environments. In this approach, instructional experts are provided with summaries of the activities of learners who interact with technology enhanced learning tools. These experts, which may include instructors, instructional designers, educational technologists, and others, use this data to gain insight into the activities of their learners. These insights lead experts to form instructional interventions which can be used to enhance the learning experience. The novel aspect of this approach is that the actions of the intelligent learning environment are now not just those of the learners and software constructs, but also those of the educational experts who may be supporting the learning process. The kinds of insights and interventions that come from application of the data-assisted approach vary with the domain being taught, the epistemology and pedagogical techniques being employed, and the particulars of the cohort being instructed. In this dissertation, three investigations using the data-assisted approach are described. The first of these demonstrates the effects of making available to instructors novel sociogram-based visualizations of online asynchronous discourse. By making instructors aware of the discussion habits of both themselves and learners, the instructors are better able to measure the effect of their teaching practice. This enables them to change their activities in response to the social networks that form between their learners, allowing them to react to deficiencies in the learning environment. Through these visualizations it is demonstrated that instructors can effectively change their pedagogy based on seeing data of their students’ interactions. The second investigation described in this dissertation is the application of unsupervised machine learning to the viewing habits of learners using lecture capture facilities. By clustering learners into groups based on behaviour and correlating groups with academic outcome, a model of positive learning activity can be described. This is particularly useful for instructional designers who are evaluating the role of learning technologies in programs as it contextualizes how technologies enable success in learners. Through this investigation it is demonstrated that the viewership data of learners can be used to assist designers in building higher level models of learning that can be used for evaluating the use of specific tools in blended learning situations. Finally, the results of applying supervised machine learning to the indexing of lecture video is described. Usage data collected from software is increasingly being used by software engineers to make technologies that are more customizable and adaptable. In this dissertation, it is demonstrated that supervised machine learning can provide human-like indexing of lecture videos that is more accurate than current techniques. Further, these indices can be customized for groups of learners, increasing the level of personalization in the learning environment. This investigation demonstrates that the data-assisted approach can also be used by application developers who are building software features for personalization into intelligent learning environments. Through this work, it is shown that a data-assisted approach to supporting instructional interventions in technology enhanced learning environments is both possible and can positively impact the teaching and learning process. By making available to instructional experts the online activities of learners, experts can better understand and react to patterns of use that develop, making for a more effective and personalized learning environment. This approach differs from traditional methods of building intelligent learning environments, which apply learning theories a priori to instructional design, and do not leverage the in situ data collected about learners