Not only is Quantum Mechanics (QM) conceptually rich, it is also a theory that physics students have found abstract and technically formidable. Nevertheless, compared to other classical topics of physics, university students’ understanding of QM has received minimal attention in the physics education literature. The principal purpose of this study was to characterize the variation in the ways that undergraduate physics students depict the basic concepts of QM and to extrapolate the results to scaffold possible changes to instructional practices at the university that provided the context for the study. In so doing, an adaptation of a developmental phenomenographic perspective was chosen. Empirically, the study was approached through in-depth interviews with 35 physics students from two Ethiopian governmental universities after they had been exposed to the traditional QM course for one-third of a semester. Interview responses were analyzed using phenomenographic approach where a picture of students’ depictions was established for each quantum concept by expounding the given responses. For each basic quantum concept addressed, the structure of the description categories was separately constructed, and overall, it was found that naive, quasi-classical ontology and/or variants of classical ways of visualization are dominant in students’ responses. For example, it was found that students’ depictions of the photon concept could be described with three distinct categories of description, which are (a) classical intuitive description, (b) mixed model description and (c) quasi-quantum model description. Similarly, the findings revealed that it is possible to establish three qualitatively different categories of description to picture students’ depictions of matter waves, namely, (a) classical and trajectory-based description, (b) an intricate blend of classical and quantum description and (c) incipient quantum model description. Likewise, it was found that students’ depictions of uncertainty principle can be described as: (a) uncertainty as classical ignorance, (b) uncertainty as measurement disturbance and (c) uncertainty as a quasi-quantum principle. With regard to learning QM, the categories of description made clear several issues: most students did not have enough knowledge to depict the basic concepts of QM properly; they were influenced by the perspective of classical physics and their perceptions in making explanations about QM; and they also applied mixed ideas, one based on their classical model and the other from newly introduced QM. These results are also supported by the findings of previous studies in similar domains. Findings from the study were used to guide the design of multiple representations-based instructions and interactive learning tutorials on the conceptual aspects of QM that has been shown to address specific difficulties identified in the study. Theoretical and practical implications of the study, as well as potential future considerations are drawn. / Mathematics, Science and Technology Education / D. Phil. (Mathematics, Science and Technology Education)

Not only is Quantum Mechanics (QM) conceptually rich, it is also a theory that physics students have found abstract and technically formidable. Nevertheless, compared to other classical topics of physics, university students’ understanding of QM has received minimal attention in the physics education literature. The principal purpose of this study was to characterize the variation in the ways that undergraduate physics students depict the basic concepts of QM and to extrapolate the results to scaffold possible changes to instructional practices at the university that provided the context for the study. In so doing, an adaptation of a developmental phenomenographic perspective was chosen. Empirically, the study was approached through in-depth interviews with 35 physics students from two Ethiopian governmental universities after they had been exposed to the traditional QM course for one-third of a semester. Interview responses were analyzed using phenomenographic approach where a picture of students’ depictions was established for each quantum concept by expounding the given responses. For each basic quantum concept addressed, the structure of the description categories was separately constructed, and overall, it was found that naive, quasi-classical ontology and/or variants of classical ways of visualization are dominant in students’ responses. For example, it was found that students’ depictions of the photon concept could be described with three distinct categories of description, which are (a) classical intuitive description, (b) mixed model description and (c) quasi-quantum model description. Similarly, the findings revealed that it is possible to establish three qualitatively different categories of description to picture students’ depictions of matter waves, namely, (a) classical and trajectory-based description, (b) an intricate blend of classical and quantum description and (c) incipient quantum model description. Likewise, it was found that students’ depictions of uncertainty principle can be described as: (a) uncertainty as classical ignorance, (b) uncertainty as measurement disturbance and (c) uncertainty as a quasi-quantum principle. With regard to learning QM, the categories of description made clear several issues: most students did not have enough knowledge to depict the basic concepts of QM properly; they were influenced by the perspective of classical physics and their perceptions in making explanations about QM; and they also applied mixed ideas, one based on their classical model and the other from newly introduced QM. These results are also supported by the findings of previous studies in similar domains. Findings from the study were used to guide the design of multiple representations-based instructions and interactive learning tutorials on the conceptual aspects of QM that has been shown to address specific difficulties identified in the study. Theoretical and practical implications of the study, as well as potential future considerations are drawn. / Mathematics, Science and Technology Education / D. Phil. (Mathematics, Science and Technology Education)

Nesta tese apresentam-se desenvolvimentos metodológicos para analisar dados com omissão e também estudos delineados para compreender os resultados de tais análises. Escrutinam-se análises de sensibilidade bayesiana e clássica para dados com respostas categorizadas sujeitas a omissão. Mostra-se que as componentes subjetivas de cada abordagem podem influenciar os resultados de maneira não-trivial, independentemente do tamanho da amostra, e que, portanto, as conclusões devem ser cuidadosamente avaliadas. Especificamente, demonstra-se que distribuições \\apriori\\ comumente consideradas como não-informativas ou levemente informativas podem, na verdade, ser bastante informativas para parâmetros inidentificáveis, e que a escolha do modelo sobreparametrizado também tem um papel importante. Quando há omissão em variáveis explicativas, também é necessário propor um modelo marginal para as covariáveis mesmo se houver interesse apenas no modelo condicional. A especificação incorreta do modelo para as covariáveis ou do modelo para o mecanismo de omissão leva a inferências enviesadas para o modelo de interesse. Trabalhos anteriormente publicados têm-se dividido em duas vertentes: ou utilizam distribuições semiparamétricas/não-paramétricas, flexíveis para as covariáveis, e identificam o modelo com a suposição de um mecanismo de omissão não-informativa, ou empregam distribuições paramétricas para as covariáveis e permitem um mecanismo mais geral, de omissão informativa. Neste trabalho analisam-se respostas binárias, combinando um mecanismo de omissão informativa com um modelo não-paramétrico para as covariáveis contínuas, por meio de uma mistura induzida pela distribuição \\apriori\\ de processo de Dirichlet. No caso em que o interesse recai apenas em momentos da distribuição das respostas, propõe-se uma nova análise de sensibilidade sob o enfoque clássico para respostas incompletas que evita suposições distribucionais e utiliza parâmetros de sensibilidade de fácil interpretação. O procedimento tem, em particular, grande apelo na análise de dados contínuos, campo que tradicionalmente emprega suposições de normalidade e/ou utiliza parâmetros de sensibilidade de difícil interpretação. Todas as análises são ilustradas com conjuntos de dados reais. / We present methodological developments to conduct analyses with missing data and also studies designed to understand the results of such analyses. We examine Bayesian and classical sensitivity analyses for data with missing categorical responses and show that the subjective components of each approach can influence results in non-trivial ways, irrespectively of the sample size, concluding that they need to be carefully evaluated. Specifically, we show that prior distributions commonly regarded as slightly informative or non-informative may actually be too informative for non-identifiable parameters, and that the choice of over-parameterized models may drastically impact the results. When there is missingness in explanatory variables, we also need to consider a marginal model for the covariates even if the interest lies only on the conditional model. An incorrect specification of either the model for the covariates or of the model for the missingness mechanism leads to biased inferences for the parameters of interest. Previously published works are commonly divided into two streams: either they use semi-/non-parametric flexible distributions for the covariates and identify the model via a non-informative missingness mechanism, or they employ parametric distributions for the covariates and allow a more general informative missingness mechanism. We consider the analysis of binary responses, combining an informative missingness model with a non-parametric model for the continuous covariates via a Dirichlet process mixture. When the interest lies only in moments of the response distribution, we consider a new classical sensitivity analysis for incomplete responses that avoids distributional assumptions and employs easily interpreted sensitivity parameters. The procedure is particularly useful for analyses of missing continuous data, an area where normality is traditionally assumed and/or relies on hard-to-interpret sensitivity parameters. We illustrate all analyses with real data sets.

Nesta tese apresentam-se desenvolvimentos metodológicos para analisar dados com omissão e também estudos delineados para compreender os resultados de tais análises. Escrutinam-se análises de sensibilidade bayesiana e clássica para dados com respostas categorizadas sujeitas a omissão. Mostra-se que as componentes subjetivas de cada abordagem podem influenciar os resultados de maneira não-trivial, independentemente do tamanho da amostra, e que, portanto, as conclusões devem ser cuidadosamente avaliadas. Especificamente, demonstra-se que distribuições \\apriori\\ comumente consideradas como não-informativas ou levemente informativas podem, na verdade, ser bastante informativas para parâmetros inidentificáveis, e que a escolha do modelo sobreparametrizado também tem um papel importante. Quando há omissão em variáveis explicativas, também é necessário propor um modelo marginal para as covariáveis mesmo se houver interesse apenas no modelo condicional. A especificação incorreta do modelo para as covariáveis ou do modelo para o mecanismo de omissão leva a inferências enviesadas para o modelo de interesse. Trabalhos anteriormente publicados têm-se dividido em duas vertentes: ou utilizam distribuições semiparamétricas/não-paramétricas, flexíveis para as covariáveis, e identificam o modelo com a suposição de um mecanismo de omissão não-informativa, ou empregam distribuições paramétricas para as covariáveis e permitem um mecanismo mais geral, de omissão informativa. Neste trabalho analisam-se respostas binárias, combinando um mecanismo de omissão informativa com um modelo não-paramétrico para as covariáveis contínuas, por meio de uma mistura induzida pela distribuição \\apriori\\ de processo de Dirichlet. No caso em que o interesse recai apenas em momentos da distribuição das respostas, propõe-se uma nova análise de sensibilidade sob o enfoque clássico para respostas incompletas que evita suposições distribucionais e utiliza parâmetros de sensibilidade de fácil interpretação. O procedimento tem, em particular, grande apelo na análise de dados contínuos, campo que tradicionalmente emprega suposições de normalidade e/ou utiliza parâmetros de sensibilidade de difícil interpretação. Todas as análises são ilustradas com conjuntos de dados reais. / We present methodological developments to conduct analyses with missing data and also studies designed to understand the results of such analyses. We examine Bayesian and classical sensitivity analyses for data with missing categorical responses and show that the subjective components of each approach can influence results in non-trivial ways, irrespectively of the sample size, concluding that they need to be carefully evaluated. Specifically, we show that prior distributions commonly regarded as slightly informative or non-informative may actually be too informative for non-identifiable parameters, and that the choice of over-parameterized models may drastically impact the results. When there is missingness in explanatory variables, we also need to consider a marginal model for the covariates even if the interest lies only on the conditional model. An incorrect specification of either the model for the covariates or of the model for the missingness mechanism leads to biased inferences for the parameters of interest. Previously published works are commonly divided into two streams: either they use semi-/non-parametric flexible distributions for the covariates and identify the model via a non-informative missingness mechanism, or they employ parametric distributions for the covariates and allow a more general informative missingness mechanism. We consider the analysis of binary responses, combining an informative missingness model with a non-parametric model for the continuous covariates via a Dirichlet process mixture. When the interest lies only in moments of the response distribution, we consider a new classical sensitivity analysis for incomplete responses that avoids distributional assumptions and employs easily interpreted sensitivity parameters. The procedure is particularly useful for analyses of missing continuous data, an area where normality is traditionally assumed and/or relies on hard-to-interpret sensitivity parameters. We illustrate all analyses with real data sets.

At first, the thesis aims at general definition of {\clq}qjustice``, it presents principles and typology of justice. Next, it explores a historical-social background of these conceptions of justice in an outline of historical eras. The second chapter introduces principles and distribution of property in social reality. The third chapter offers an overview of John Rawls´ conception of justice, which is one of the most important contemporary accounts. The fourth chapter deals with a social justice on an international level and enlarges upon Rawls´ theory discussing questions of global justice posed by other authors.

Whoonga is a relatively new addition into the drug market. The need for this study was prompted by the devastating effects this new arrival has had in the lives of young people addicted to it. The purpose of this study was to investigate the effects of whoonga on the learning of affected youth in Kwa-Dabeka Township. This study adopted a qualitative method and employed a phenomenological approach to explore the experiences of participants with regard to whoonga use and their learning. Data was collected through purposive sampling. Interviews were conducted, using semi-structured and unstructured questions with the help of an interview guide. Observations were also conducted to collect more data. This was done in the classroom during teaching and learning, as well as outside the classroom during recess. The study employed a social learning theoretical framework on the experiences of participants with regard to the use of whoonga. Four participants from one high school in Kwa-Dabeka Township were involved in the study. Themes that emerged from the study were that all the participants were totally ignorant of what they were getting themselves into before they started using whoonga. Peer pressure, coupled with curiosity made their decision to use whoonga easy. Challenges that participants face now on daily basis are far beyond their young age. The findings have indicated that learning is a situation of near impossibility for the participants. The findings have also depicted a picture of young people who are trapped in a vicious cycle of one of life‟s harshest living conditions in terms of their encounters with parents, school and the communities they come from. Despite their hopes for a brighter future one day, participants see no end in sight for their suffering at the hands of this unforgiving, destructive drug at this point in time. / Educational Studies / M. Ed. (Socio-Education)

Generation Z har under sin korta tid som investerare på den finansiella marknaden varit med om en säregen börskrasch till följd av coronapandemin 2020. Börskraschen 2020 var unik i förhållande till tidigare börskrascher såsom IT-kraschen 2000 och finanskrisen 2008 eftersom marknaden återhämtade sig och nådde nya rekordnivåer inom ett halvår. Samtidigt stod svenska investerare under de första månaderna av 2022 inför ett nytt ras där den genomsnittliga aktien på Stockholmsbörsen sjunkit med ungefär 36% från dess högsta nivå under perioden 2020–2022 till följd av en hög inflationstakt samt Rysslands invasion av Ukraina. Forskningen kring den yngsta gruppen investerare, generation Z, som saknar erfarenhet av långvariga osäkra marknadsförhållanden är bristfällig och ger därmed upphov till en ny grupp investerare att studera. Studiens syfte är att studera hur riskbenägenhet samvarierar med börspsykologiska bias, kognitiv reflektionsförmåga och erfarenhet från tidigare finansiella kriser samt hur dessa faktorer skiljer sig mellan generation Z och tidigare generationer. Studien finner att generation Z till större del faller för samtliga börspsykologiska bias förutom överreaktion och visar på ett flertal signifikanta skillnader mellan generation Z och äldre generationer. Resultatet visar även att anchoring och konfirmeringsbiassamvarierar signifikant med riskbenägenhet och att den största skillnaden mellan generation Z och äldre generationer kan hänföras till börspsykologiska bias. / Generation Z has during their short time as investors experienced a market crash due to the covid-pandemic in 2020 but also a quick recovery in the same year where the market reached a new all-time high. The market crash in 2020 was unique compared to the crash following the burst of the Dot-com bubble in 2000 and the financial crisis in 2008 because of the quick recovery of the stock market that led to new all-time highs. However, during the first months of 2022, Swedish investors were once again experiencing a market crash where the average stock on the Stockholm stock exchange had lost more than 36% of its value since their peak between 2020-2022 due to high inflation and Russia’s invasion of Ukraine. The youngest group of investors, generation Z, lacks experience from long-lasting uncertainties in the stock market. Research regarding generation Z is insufficient which gives rise to a new group of investors to study. The purpose of the thesis is to study the relationship between risk tolerance and psychological biases, cognitive reflection and experience from previous stock market crashes and how these factors differ between generation Z and older generations. The thesis finds that generation Z is indicated to be more affected by every psychological bias except overreaction and finds several significant differences between generation Z and the older generations. The result also shows that confirmation bias and anchoring have a significant relationship to risk tolerance and that the largest differences between generation Z and older generations are derived from psychological biases.

The popularization of academic spaces that combine Buddhist philosophy with the literature of the Romantic period – a discipline I refer to as Buddhist Romantic Studies – have exposed the lack of scholarly attention Samuel Taylor Coleridge and The Rime of the Ancient Mariner have received within such studies. Validating Coleridge’s right to exist within Buddhist Romantic spheres, my thesis argues that Coleridge was cognizant of Buddhism through historical and textual encounters. To create a space for The Rime within Buddhist Romantic Studies, my thesis provides an interpretation of the poem that centers on the concept of prajna, or wisdom, as a vital tool for cultivating the mind. Focusing on prajna, I argue that the Mariner’s didactic story traces his cognitive voyage from ignorance to enlightenment. By examining The Rime within the framework of Buddhism, readers will also be able to grasp the importance of cultivating the mind and transcending ignorance.

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