This research aims to investigate an assertion, endorsed by a range of commentators, that multimedia teaching and learning approaches encourage learners to adopt a richer, creative and deeper level of understanding and participation within the learning environment than traditional teaching and learning methods. The thesis examines this assertion by investigating one type of multimedia activity defined (for the purposes of this research) as a digital audio video assessment (DAVA). Data was collected using a constructivist epistemology, interpretative and naturalistic perspective using primarily a qualitative methodology. Three types of data collection methods were used to collect data from thirteen Diploma of Event Management students from William Angliss TAFE. Firstly, participants completed the Biggs Study Process Questionnaire (2001) which is a predictor of deep and surface learning preference. Each participant then engaged in a semi-structured interview that elicited participant's self-declared learning preferences and their approaches to completion of the DAVA. These data sources were then compared. Six factors that are critical in informing the way that the participants approached the DAVA emerged from the analysis of the data. Based on these findings it is concluded that the DAVA does not restrict, inhibit or negatively influence a participants learning preference. Learners with a pre-existing, stable learning preference are likely to adopt a learning approach that is consisten t with their preference. Participants that have a learning preference that is less stable (more flexible) may adopt either a surface or deep approach depending on the specific task, activity or assessment.

Published Article / Work-Integrated Learning (WIL) is a form of Experiential Learning (EL) which implies learning by experience. This article represents the findings of a study regarding the roles of WIL and how such roles can be quantified when measured against the achievement of Critical Cross-Field Outcomes (CCFOs). The study was based on an empirical mixed-method triangulation, which allowed the researchers to use both qualitative and quantitative methods to address the research problem. The sample size is 35, constituting the third and fourth-year groups in the Hospitality Management programme at a higher education institution in South Africa. The results of the quantitative study indicate that the students have identified various roles for WIL whereas the quantitative investigation revealed that students are of the opinion that WIL contributes significantly towards the achievement of CCFOs. WIL therefore contributes to skills development in general and to the attainment of skills and attributes as represented by the CCFOs in particular. Recommendations regarding the implications of the study are made for curriculation purposes as well as for credit values to be attached to WIL.

Through the development and implementation of modified learning contexts, the current study encouraged undergraduate teacher education students to modify their approaches to learning by reducing their reliance on surface approaches and progressively adopting deeper approaches. This outcome was considered desirable because students who employed deep approaches would exit the course having achieved higher quality learning than those who relied primarily on surface approaches. It was expected that higher quality learning in a preservice teacher education program would also translate into greater self-confidence in the management of teaching tasks, leading to improvements in students� teaching self-efficacy beliefs. Altered learning contexts were developed through the application of action research methodology involving core members of the teaching team. Learning activities were designed with a focus on co-operative small-group problem-based learning, which included multiple subtasks requiring variable outcome presentation modes. Linked individual reflection was encouraged by personal learning journals and learning portfolios. Students also provided critical analyses of their own learning during the completion of tasks, from both individual and group perspectives. Assessment methods included lecturer, peer and self-assessment, depending on the nature of the learning task. Often these were integrated, so that subtasks within larger ones were assessed using combinations of methods. Learning approach theorists (Biggs, 1993a, 1999; Entwistle, 1986, 1998; Prosser & Trigwell, 1999; Ramsden, 1992, 1997) contend that learning outcomes are directly related to the learning approaches used in their development. They further contend that the approach adopted is largely a result of students� intent, which in turn, is influenced by their perception of the learning context. The present study therefore aimed to develop an integrated and pervasive course-based learning context, constructively aligned (after: Biggs, 1993a, 1996), achievable within the normal constraints of a university program, that would influence students� adoption of deep learning approaches. The cognitive processes students used in response to the altered contexts were interpreted in accordance with self-regulatory internal logic (after: Bandura, 1986, 1991b; Zimmerman, 1989, 1998b). Longitudinal quasi-experimental methods with repeated measures on non-equivalent dependent variables were applied to three cohorts of students. Cohort 1 represented the contrast group who followed a traditional program. Cohort 2 was the main treatment group to whom the modified program was presented. Cohort 3 represented a comparison group that was also presented with the modified program over a shorter period. Student data on learning approach, teaching efficacy and academic attributions were gathered from repeated administrations of the Study Process Questionnaire (Biggs, 1987b), Teacher Efficacy Scale (Gibson & Dembo, 1984) and Multidimensional-Multiattributional Causality Scale (Lefcourt, 1991). In addition, reflective journals, field observations and transcripts of interviews undertaken at the beginning and conclusion of the course, were used to clarify students� approaches to learning and their responses to program modifications. Analyses of learning approaches adopted by Cohorts 1 and 2 revealed that they both began their course predominantly using surface approaches. While students in Cohort 1 completed the course with approximately equal reliance on deep and surface approaches, students in Cohort 2 reported a predominant use of deep approaches on course completion. The relative impact of the modified learning context on students with differing approaches to learning in this cohort were further explained through qualitative data and cluster analyses. The partial replication of the study with Cohort 3, across the first three semesters of their program, produced similar effects to those obtained with Cohort 2. The analyses conducted with teaching efficacy data indicated a similar pattern of development for all cohorts. Little change in either personal or general dimensions was noted in the first half of the program, followed by strong growth in both, in the latter half. While a relationship between learning approach usage and teaching efficacy was not apparent in Cohort 1, developmental path and mediation analyses indicated that the use of deep learning approaches considerably influenced the development of personal teaching efficacy in Cohort 2. The current research suggests that value lies in the construction of learning environments, in teacher education, that enhance students� adoption of deep learning approaches. The nature of the task is complex, multifaceted and context specific, most likely requiring the development of unique solutions in each environment. Nevertheless, this research demonstrates that such solutions can be developed and applied within the prevailing constraints of pre-existing course structures.

Through the development and implementation of modified learning contexts, the current study encouraged undergraduate teacher education students to modify their approaches to learning by reducing their reliance on surface approaches and progressively adopting deeper approaches. This outcome was considered desirable because students who employed deep approaches would exit the course having achieved higher quality learning than those who relied primarily on surface approaches. It was expected that higher quality learning in a preservice teacher education program would also translate into greater self-confidence in the management of teaching tasks, leading to improvements in students� teaching self-efficacy beliefs. Altered learning contexts were developed through the application of action research methodology involving core members of the teaching team. Learning activities were designed with a focus on co-operative small-group problem-based learning, which included multiple subtasks requiring variable outcome presentation modes. Linked individual reflection was encouraged by personal learning journals and learning portfolios. Students also provided critical analyses of their own learning during the completion of tasks, from both individual and group perspectives. Assessment methods included lecturer, peer and self-assessment, depending on the nature of the learning task. Often these were integrated, so that subtasks within larger ones were assessed using combinations of methods. Learning approach theorists (Biggs, 1993a, 1999; Entwistle, 1986, 1998; Prosser & Trigwell, 1999; Ramsden, 1992, 1997) contend that learning outcomes are directly related to the learning approaches used in their development. They further contend that the approach adopted is largely a result of students� intent, which in turn, is influenced by their perception of the learning context. The present study therefore aimed to develop an integrated and pervasive course-based learning context, constructively aligned (after: Biggs, 1993a, 1996), achievable within the normal constraints of a university program, that would influence students� adoption of deep learning approaches. The cognitive processes students used in response to the altered contexts were interpreted in accordance with self-regulatory internal logic (after: Bandura, 1986, 1991b; Zimmerman, 1989, 1998b). Longitudinal quasi-experimental methods with repeated measures on non-equivalent dependent variables were applied to three cohorts of students. Cohort 1 represented the contrast group who followed a traditional program. Cohort 2 was the main treatment group to whom the modified program was presented. Cohort 3 represented a comparison group that was also presented with the modified program over a shorter period. Student data on learning approach, teaching efficacy and academic attributions were gathered from repeated administrations of the Study Process Questionnaire (Biggs, 1987b), Teacher Efficacy Scale (Gibson & Dembo, 1984) and Multidimensional-Multiattributional Causality Scale (Lefcourt, 1991). In addition, reflective journals, field observations and transcripts of interviews undertaken at the beginning and conclusion of the course, were used to clarify students� approaches to learning and their responses to program modifications. Analyses of learning approaches adopted by Cohorts 1 and 2 revealed that they both began their course predominantly using surface approaches. While students in Cohort 1 completed the course with approximately equal reliance on deep and surface approaches, students in Cohort 2 reported a predominant use of deep approaches on course completion. The relative impact of the modified learning context on students with differing approaches to learning in this cohort were further explained through qualitative data and cluster analyses. The partial replication of the study with Cohort 3, across the first three semesters of their program, produced similar effects to those obtained with Cohort 2. The analyses conducted with teaching efficacy data indicated a similar pattern of development for all cohorts. Little change in either personal or general dimensions was noted in the first half of the program, followed by strong growth in both, in the latter half. While a relationship between learning approach usage and teaching efficacy was not apparent in Cohort 1, developmental path and mediation analyses indicated that the use of deep learning approaches considerably influenced the development of personal teaching efficacy in Cohort 2. The current research suggests that value lies in the construction of learning environments, in teacher education, that enhance students� adoption of deep learning approaches. The nature of the task is complex, multifaceted and context specific, most likely requiring the development of unique solutions in each environment. Nevertheless, this research demonstrates that such solutions can be developed and applied within the prevailing constraints of pre-existing course structures.

Titel: Redovisningsstudenter & generativ AI  Nivå: Examensarbete på grundnivå (kandidatexamen) i ämnet företagsekonomi. Författare: Jennifer Roos och Josefine Olsson Handledare: Jan Svanberg Datum: 2024 – maj Syfte: Undersöka hur redovisningsstudenter med olika inlärningsstrategier (ytinlärning och djupinlärning) använder generativ AI i sina studier samt att analysera hur generativ AI bidrar till studenternas lärande.    Metod: Studien utgår från en positivistisk forskningsfilosofi och en deduktiv forskningsansats. Metoden består av en kvantitativ forskningsdesign med en tvärsnittsdesign i form av en enkätundersökning som utformar studiens primärdata bestående av 62 respondenter, varav 10 respondenter uteslöts och räknas som bortfall. Datamaterialet har kodats och analyserats i statistikprogrammet SPSS.   Resultat och slutsats: Studiens resultat indikerar att det finns en jämn spridning mellan inlärningsstrategierna yt- och djupinlärning hos redovisningsstudenter samt att fåtalet redovisningsstudenter tillhör båda inlärningsstrategierna. Resultatet visar att generativ AI kan användas i både ytinlärning och djupinlärning och tenderar att accentuera den aktuella inlärningsstrategin.    Examensarbetes bidrag: Studien bidrar med ny, högaktuell och viktig forskning till forskningsgapet gällande hur generativ AI påverkar redovisningsstudenters inlärningsstrategi. Insikterna från studien bidrar till en ökad förståelse kring utformningen av redovisningsutbildningen för att förbereda redovisningsstudenter inför yrket.   Förslag till fortsatt forskning: Framtida forskning kan utöka urvalet för att bättre representera populationen, redovisningsstudenter. Dessutom bör framtida forskning utforska hur andra inlärningsstrategier kan påverka användningen av generativ AI samt undersöka samband mellan variabler som kön, ålder, geografisk plats och kursämne för att identifiera likheter, skillnader och mönster.    Nyckelord: Chatbotar, Djupinlärning, Generativ AI, Inlärningsstrategier, Redovisningsstudenter & Ytinlärning. / Title: Accounting Students & generative AI  Level: Student thesis, final assignment for Bachelor Degree in Business Administration. Author: Jennifer Roos and Josefine Olsson Supervisor: Jan Svanberg Date: 2024 – May                                                     Aim: To investigate how accounting students with different learning strategies (surface learning and deep learning) use generative AI in their studies and to analyze how generative AI contributes to students’ learning.    Method: The study is based on a positivist research philosophy and a deductive research approach. The method is a quantitative research design with a cross-sectional design in the form of a questionnaire that forms the study's primary data consisting of 62 respondents, of which 10 respondents were excluded and counted as non-valid. The data has been coded and analyzed in the statistical program SPSS.   Results and conclusions: The results of the study indicate that there is an even spread between the learning strategies, surface- and deep learning, in accounting students and that the few accounting students belong to both learning strategies. The result shows that generative AI can be used for both surface learning and deep learning and tends to accentuate the current learning strategy.   Contribution of the thesis: The study contributes to new, highly current and important research to the research gap regarding how generative AI affects the learning strategy of accounting students. The insights from the study contribute to an increased understanding of the design of accounting education to prepare accounting students for the profession.   Suggestions for future research: Future research could expand the sample to better represent the population, accounting students. Additionally, future research should explore how other learning strategies may influence the use of generative AI as well as examine relationships between variables such as gender, age, geographic location, and course subject to identify similarities, differences, and patterns. Key words: Accounting Students, Chatbots, Deep learning, Generative AI, Learning strategies & Surface learning.

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The advancements in robotic perception in the recent years have empowered robots to better execute tasks in various environments. The perception of objects in the robot work space significantly relies on how sensory data is represented. In this context, 3D models of object’s surfaces have been studied as a means to provide useful insights on shape of objects and ultimately enhance robotic perception. This involves several challenges, because sensory data generally presents artifacts, such as noise and incompleteness. To tackle this problem, we employ Gaussian Process Implicit Surface (GPIS), a non-parametric probabilistic reconstruction of object’s surfaces from 3D data points. This thesis investigates different configurations for GPIS, as a means to tackle the extraction of shape information. In our approach we interpret an object’s surface as the level-set of an underlying sparse Gaussian Process (GP) with variational formulation. Results show that the variational formulation for sparse GP enables a reliable approximation to the full GP solution. Experiments are performed on a synthetic and a real sensory data set. We evaluate results by assessing how close the reconstructed surfaces are to the ground-truth correspondences, and how well objects from different categories are clustered based on the obtained representation. Finally we conclude that the proposed solution derives adequate surface representations to reason about object shape and to discriminate objects based on shape information. / Framsteg inom robotperception de senaste åren har resulterat i robotar som är bättre på attutföra uppgifter i olika miljöer. Perception av objekt i robotens arbetsmiljö är beroende avhur sensorisk data representeras. I det här sammanhanget har 3D-modeller av objektytorstuderats för att ge användbar insikt om objektens form och i slutändan bättre robotperception. Detta innebär flera utmaningar, eftersom sensoriska data ofta innehåller artefakter, såsom brus och brist på data. För att hantera detta problem använder vi oss av Gaussian Process Implicit Surface (GPIS), som är en icke-parametrisk probabilistisk rekonstruktion av ett objekts yta utifrån 3D-punkter. Detta examensarbete undersöker olika konfigurationer av GPIS för att på detta sätt kunna extrahera forminformation. I vår metod tolkar vi ett objekts yta som nivåkurvor hos en underliggande gles variational Gaussian Process (GP) modell. Resultat visar att en gles variational GP möjliggör en tillförlitlig approximation av en komplett GP-lösningen. Experiment utförs på ett syntetisk och ett reellt sensorisk dataset. Vi utvärderar resultat genom att bedöma hur nära de rekonstruerade ytorna är till grundtruth- korrespondenser, och hur väl objektkategorier klustras utifrån den erhållna representationen. Slutligen konstaterar vi att den föreslagna lösningen leder till tillräckligt goda representationer av ytor för tolkning av objektens form och för att diskriminera objekt utifrån forminformation.