Amara Atif

Assuring graduate competency: a technology acceptance model for course guide tools

Abstract Higher education institutions typically express the quality of their degree programs by describing the qualities, skills, and understanding their students possess upon graduation. One promising instructional design approach to facilitate institutions’ efforts to deliver graduates with the appropriate knowledge and competencies is curriculum mapping. To support the complex activity of curriculum mapping and to address existing problems associated with current practices around unit guides, that many Australian higher education institutions are developing unit guide information systems (UGISs). This study examines factors influencing the acceptance and use of UGIS by unit conveners and academics. This study proposed a model for the acceptance of UGIS, which integrated key constructs from the technology acceptance model (TAM), social cognitive theory and model of PC utilization including seven main factors: perceived usefulness, perceived ease of use, attitude towards using the UGIS, intention to use the UGIS, social influence, unit guide specific self-efficacy, and unit guide specific anxiety. The model was tested on a sample of 134 unit guide users from 39 Australian universities and analyzed using structural equation modeling and partial least squares methods. Analysis showed that attitude, perceived usefulness, and perceived ease of use from the basic TAM model contributed significantly to explain the intention of academics and unit conveners to use UGIS. In addition, the integration of self-efficacy, anxiety and social influence as constructs were found to improve the fit of the model. Implications of the results are discussed within the context of unit guides and curriculum mapping.

Knowledge Acquisition for Learning Analytics: Comparing Teacher-Derived, Algorithm-Derived, and Hybrid Models in the Moodle Engagement Analytics Plugin

One of the promises of big data in higher education (learning analytics) is being able to accurately identify and assist students who may not be engaging as expected. These expectations, distilled into parameters for learning analytics tools, can be determined by human teacher experts or by algorithms themselves. However, there has been little work done to compare the power of knowledge models acquired from teachers and from algorithms. In the context of an open source learning analytics tool, the Moodle Engagement Analytics Plugin, we examined the ability of teacher-derived models to accurately predict student engagement and performance, compared to models derived from algorithms, as well as hybrid models. Our preliminary findings, reported here, provided evidence for the fallibility and strength of teacher- and algorithm-derived models, respectively, and highlighted the benefits of a hybrid approach to model- and knowledge-generation for learning analytics. A human in the loop solution is therefore suggested as a possible optimal approach.

Towards an ontology-based approach to knowledge management of graduate attributes in higher education

Knowledge around graduate attributes (GAs) is an area in need of knowledge management strategies. GAs are the qualities and skills that a university agrees its students should develop during their time with the institution. The importance of GAs and ensuring they are embedded and assessed is widely accepted across higher education. This research paper uses Grounded Theory and Network Maps to gain insights into the issues of similarities and differences in the discourse around our sample universities. To cover these two perspectives, we had two researchers involved in data analysis, one with the goal of distilling key ideas and identifying similarities and the other with the goal of untangling and drawing out differences. There is no unified taxonomy of managing GAs. The motivation to create such ontology is to push the standardization process that will enable the connection among academic systems and improve educational workflows, communication, and collaboration between universities.