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The concept of deep learning is not new to higher education. However, deep learning has drawn more attention in recent years as institutions attempt to tap their student’s full learning potential. To more fully develop student talents, many campuses are shifting from a traditional passive, instructor-dominated pedagogy to active, learner-centered activities. Using exploratory and confirmatory factor analysis on multiple years of data from the National Survey of Student Engagement, this study examines the structure and characteristics of items about student uses of deep approaches to learning. Institutions and researchers can use the resulting scales to assess and investigate deep approaches to learning. Deep Approaches to Learning 3 Measuring Deep Approaches to Learning Using the National Survey of Student Engagement Colleges and universities are devoting substantial effort to design active, learner-centered teaching and learning environments. Findings from the National Survey of Student Engagement

This paper advocates the use of repeated applications of inventories of approaches to learning as an appropriate technique for the evaluation of educational innovations. The method is particularly appropriate for innovations aiming to introduce more meaningful forms of learning as this is a dimension measured by these instruments. The paper gives case studies showing how the measure can be used for innovations introduced in naturalistic settings. There is a voluminous literature on educational innovations. Different teaching methods, models of teaching, the latest educational media, new curricula, alternative assessment methods and many other forms of innovation have been proposed as leading to better teaching and improvements in student learning outcomes. For such claims to be believed they need some form of evaluation. The traditional approach to seeking evidence is to use the science-based experiment and control method. This approach, though, has been subject to criticism and suffers from limitations where the aim is to introduce an innovation into a genuine educational setting, rather than to conduct a laboratory-type trial. The first difficulty lies in ensuring that the experiment and control are genuinely comparable. Educational media comparison studies, for example, have been criticized on this basis by a

Abstract—New forms of computer-mediated, online learning can benefit from new forms of assessment that fit the medium and the pedagogical style of the online environment. This paper investigates students ’ learning styles and learning strategies in taking online collaborative exams. Applying constructivist and collaborative learning theories, the collaborative examination features students ’ active participation in various phases of the exam process through small group activities online. Students ’ learning strategies, including deep learning and collaborative learning, are investigated using a 1 2 3 field quasi-experiment to compare the team-based collaborative online exam with the traditional in-class exam and with the participatory exam, where students participate in the online exam processes individually. Data analysis using results from 485 students indicates that collaborative examinations significantly reduced surface learning in exam study, enhanced interactions and the sense of an online learning community, and increased perceived learning. The results also suggest learning predispositions were significantly correlated with exam study strategies, and provide indications of their effects on learning strategies. Index Terms—Collaborative examinations, collaborative learning, deep learning, online learning, peer assessment, surface learning. With over three million university students taking online courses in the US alone in 2005, and with

Dissertation submitted in fulfilment of the requirements for the degree

Abstract: This paper looks at the role of student awareness of learning styles in helping to both reflect on those styles and to consider their role in a piece of assessed work. Entwistle’s Approaches to Study Skills Inventory for Students (ASSIST) is used in these two ways. The first is by getting the students to consider each of the learning styles suggested by ASSIST. The second is by considering whether those results help inform the design and construction of individual learning environments(ILE). Students undertaking a unit in Human Computer Interaction were involved in the study. A range of measures were used to capture the participants responses to their learning styles. These included: reflective journals; a survey; ILE development documentation and the individual learning environment itself. All except the survey were assessed pieces of work. The details of the task of constructing the ILE are presented followed by an initial learning style profile of the respondents. Student comments on the approach and results are discussed and conclusions are drawn on the relevance of this measure of learning style to the design of individual learning environments.

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processing taxonomy, developed in the 1970s and 80s by two Australian academics—John Biggs and Kevin Collis, categorises mental activity by quantity and quality attributes of the activities required by students or by the observable products of student work. This taxonomy has been used in asTTle to categorise student performance on every task in Reading/Pänui and Mathematics/Pängarau. This report explains the SOLO taxonomy and its psychological basis, and provides examples of using SOLO in assessment and education in general. Other technical reports will examine the performance of students by the SOLO taxonomy categories.

This thesis is dedicated to my wife Gail and my children, Mark and Lauren. They have watched me spend endless hours at the computer instead of with them. They have shared in my journey and always helped me when the road was hard. They have sacrificed as much, if not more, than I have. I hope this work justifies their support. ii This study examines the ability of online distance education courses using CMC and constructivist assessment tools to support cognitive presence and deep learning. Four online focus groups were conducted, three among graduate students and one among instructors who have respectively taken and delivered online courses in the Master of Distance Education program at Athabasca University. Transcripts of the focus groups were analyzed with the objective of developing a grounded conceptual model. The learning experiences, as described by the participants themselves, have shown that deep learning and

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