ePortfolio use by university students in Australia: A review of the Australian ePortfolio Project

In October 2008, the Australian Learning and Teaching Council (ALTC) released the final report for the commissioned project ePortfolio use by university students in Australia: Informing excellence in policy and practice. The Australian ePortfolio Project represented the first attempt to examine the breadth and depth of ePortfolio practice in the Australian higher education sector. The research activities included surveys of stakeholder groups in learning and teaching, academic management and human resource management, with respondents representing all Australian universities; a series of focus groups and semi-structured interviews which sought to explore key issues in greater depth; and surveys designed to capture students’ pre-course expectations and their post-course experiences of ePortfolio learning. Further qualitative data was collected through interviews with ‘mature users’ of ePortfolios. Project findings revealed that, while there was a high level of interest in the use of ePortfolios in terms of the potential to help students become reflective learners who were conscious of their personal and professional strengths and weaknesses, the state of play in Australian universities was very fragmented. The project investigation identified four individual, yet interrelated, contexts where strategies may be employed to support and foster effective ePortfolio practice in higher education: government policy, technical standards, academic policy, and learning and teaching. Four scenarios for the future were also presented with the goal of stimulating discussion about opportunities for stakeholder engagement. It is argued that the effective use of ePortfolios requires open dialogue and collaboration between the different stakeholders across this range of contexts.

Students’ conceptual knowledge about workplace pedagogies and applications to learning in the workplace

The development of effective workplace pedagogies is integral to work-integrated and work-based learning. The workplace pedagogies that facilitate and support learning include: activities in which individuals engage such as daily work practices, questioning; observing, and listening; interactions with more experienced workers through coaching and modelling; and referencing documented procedures. Each of these dimensions is significant in enhancing processes of workplace learning. Learning can be optimised when these pedagogies are appropriately embedded in the context of and process of participating in normal work activities. Yet learners need to understand the nature of these pedagogies, and how and for what purposes to use these to achieve a range of learning outcomes. This is because it is the worker-learners who play the key roles in the process and outcomes of learning through work. A pilot study was conducted on students’ conceptions of how each of these dimensions of workplace pedagogy help their learning, by providing examples of learning from these sources; and stating their preferences for learning in the workplace. A sample of seventeen students, enrolled in the second year of a Diploma in Nursing course at a Technical and Further Education institution, participated in a survey intended to capture these conceptions and the importance attached to each of them. The findings indicate that these students have basic understanding of how each of seven workplace pedagogic practices can contribute to their learning. They reported relying mostly on daily practices, observing and listening to others, modelling, coaching, and other workers. Their selection of these contributions emphasise significant opportunities for guided learning by others, yet suggest fewer student-initiated interactions, less intensity in interactions, and likelihood that learning is more passive. The data also suggests that these students rely mostly on using academic learning skill, and limited workplace learning skills. It is proposed, therefore, that the knowledge and understandings about workplace learning and pedagogies might be best embedded in the full curriculum and not become add-on shortly before students go on work placement. This approach will allow students to appreciate the significance and use of workplace pedagogies for learning.

Using automated text analysis to evaluate students’ conceptual understanding

Background: A major challenge for assessing students’ conceptual understanding of STEM subjects is the capacity of assessment tools to reliably and robustly evaluate student thinking and reasoning. Multiple-choice tests are typically used to assess student learning and are designed to include distractors that can indicate students’ incomplete understanding of a topic or concept based on which distractor the student selects. However, these tests fail to provide the critical information uncovering the how and why of students’ reasoning for their multiple-choice selections. Open-ended or structured response questions are one method for capturing higher level thinking, but are often costly in terms of time and attention to properly assess student responses. Purpose: The goal of this study is to evaluate methods for automatically assessing open-ended responses, e.g. students’ written explanations and reasoning for multiple-choice selections. Design/Method: We incorporated an open response component for an online signals and systems multiple-choice test to capture written explanations of students’ selections. The effectiveness of an automated approach for identifying and assessing student conceptual understanding was evaluated by comparing results of lexical analysis software packages (Leximancer and NVivo) to expert human analysis of student responses. In order to understand and delineate the process for effectively analysing text provided by students, the researchers evaluated strengths and weakness for both the human and automated approaches. Results: Human and automated analyses revealed both correct and incorrect associations for certain conceptual areas. For some questions, that were not anticipated or included in the distractor selections, showing how multiple-choice questions alone fail to capture the comprehensive picture of student understanding. The comparison of textual analysis methods revealed the capability of automated lexical analysis software to assist in the identification of concepts and their relationships for large textual data sets. We also identified several challenges to using automated analysis as well as the manual and computer-assisted analysis. Conclusions: This study highlighted the usefulness incorporating and analysing students’ reasoning or explanations in understanding how students think about certain conceptual ideas. The ultimate value of automating the evaluation of written explanations is that it can be applied more frequently and at various stages of instruction to formatively evaluate conceptual understanding and engage students in reflective

Insights into students' conceptual understanding using textual analysis: A case study in signal processing

Concept inventory tests are one method to evaluate conceptual understanding and identify possible misconceptions. The multiple-choice question format, offering a choice between a correct selection and common misconceptions, can provide an assessment of students' conceptual understanding in various dimensions. Misconceptions of some engineering concepts exist due to a lack of mental frameworks, or schemas, for these types of concepts or conceptual areas. This study incorporated an open textual response component in a multiple-choice concept inventory test to capture written explanations of students' selections. The study's goal was to identify, through text analysis of student responses, the types and categorizations of concepts in these explanations that had not been uncovered by the distractor selections. The analysis of the textual explanations of a subset of the discrete-time signals and systems concept inventory questions revealed that students have difficulty conceptually explaining several dimensions of signal processing. This contributed to their inability to provide a clear explanation of the underlying concepts, such as mathematical concepts. The methods used in this study evaluate students' understanding of signals and systems concepts through their ability to express understanding in written text. This may present a bias for students with strong written communication skills. This study presents a framework for extracting and identifying the types of concepts students use to express their reasoning when answering conceptual questions.