Peer assessment is a process where learners evaluate and provide feedback on one another’s performance, which is critical to the student learning process. Earlier research has shown that it can improve student learning outcomes in various settings, including the setting of engineering education, in which collaborative teaching and learning activities are common. Peer assessment activities in computer-supported collaborative learning (CSCL) settings are becoming more and more common. When using digital technologies for performing these activities, much student data (e.g., peer feedback text entries) is generated automatically. These large data sets can be analyzed (through e.g., computational methods) and further used to improve our understanding of how students regulate their learning in CSCL settings in order to improve their conditions for learning by for example, providing in-time feedback. Yet there is currently a need to automatise the coding process of these large volumes of student text data since it is a very time- and resource consuming task. In this regard, the recent development in machine learning could prove beneficial. To understand how we can harness the affordances of machine learning technologies to classify student text data, this thesis examines the application of five models on a data set containing peer feedback from 231 students in the settings of a large technical university course. The models used to evaluate on the dataset are: the traditional models Multi Layer Perceptron (MLP), Decision Tree and the transformers-based models BERT, RoBERTa and DistilBERT. To evaluate each model’s performance, Cohen’s κ, accuracy, and F1-score were used as metrics. Preprocessing of the data was done by removing stopwords; then it was examined whether removing them improved the performance of the models. The results showed that preprocessing on the dataset only made the Decision Tree increase in performance while it decreased on all other models. RoBERTa was the model with the best performance on the dataset on all metrics used. Explainable artificial intelligence (XAI) was used on RoBERTa as it was the best performing model and it was found that the words considered as stopwords made a difference in the prediction. / Kamratbedömning är en process där eleverna utvärderar och ger feedback på varandras prestationer, vilket är avgörande för elevernas inlärningsprocess. Tidigare forskning har visat att den kan förbättra studenternas inlärningsresultat i olika sammanhang, däribland ingenjörsutbildningen, där samarbete vid undervisning och inlärning är vanligt förekommande. I dag blir det allt vanligare med kamratbedömning inom datorstödd inlärning i samarbete (CSCL). När man använder digital teknik för att utföra dessa aktiviteter skapas många studentdata (t.ex. textinlägg om kamratåterkoppling) automatiskt. Dessa stora datamängder kan analyseras (genom t.ex, beräkningsmetoder) och användas vidare för att förbättra våra kunskaper om hur studenterna reglerar sitt lärande i CSCL-miljöer för att förbättra deras förutsättningar för lärande. Men för närvarande finns det ett stort behov av att automatisera kodningen av dessa stora volymer av textdata från studenter. I detta avseende kan den senaste utvecklingen inom maskininlärning vara till nytta. För att förstå hur vi kan nyttja möjligheterna med maskininlärning teknik för att klassificera textdata från studenter, undersöker vi i denna studie hur vi kan använda fem modeller på en datamängd som innehåller feedback från kamrater till 231 studenter. Modeller som används för att utvärdera datasetet är de traditionella modellerna Multi Layer Perceptron (MLP), Decision Tree och de transformer-baserade modellerna BERT, RoBERTa och DistilBERT. För att utvärdera varje modells effektivitet användes Cohen’s κ, noggrannhet och F1-poäng som mått. Förbehandling av data gjordes genom att ta bort stoppord, därefter undersöktes om borttagandet av dem förbättrade modellernas effektivitet. Resultatet visade att förbehandlingen av datasetet endast fick Decision Tree att öka sin prestanda, medan den minskade för alla andra modeller. RoBERTa var den modell som presterade bäst på datasetet för alla mätvärden som användes. Förklarlig artificiell intelligens (XAI) användes på RoBERTa eftersom det var den modell som presterade bäst, och det visade sig att de ord som ansågs vara stoppord hade betydelse för prediktionen.

<p>  </p> <p>At the highest level, this dissertation is a case study on how bias can become encoded into the tools used to measure a construct and into the very definition of the construct itself. In this case, the construct is spatial ability. This dissertation focuses on the validity and accuracy of spatial tests and illuminates gender bias that is interwoven with the history of spatial testing. </p> <p><br></p> <p>First, I present a critical analysis of the graphical imagery used in spatial tests and explain why the imagery may be unclear and lead the tests to be inaccurate. I analyzed a collection of research in which researchers modified the stimuli used in spatial tests and found that the tests became easier when the imagery was made clearer. Thus, I conclude that imagery presentation impacts test difficulty, a likely example of construct-irrelevant variance which may reduce the validity of some spatial skills assessments and introduce bias in favor of individuals with past experience in engineering graphics, who historically are more likely to be men. </p> <p><br></p> <p>Second, I make a critical review of gender differential research in spatial skills. I argue that the construct of “spatial ability” has been co-constructed with gender, in that it has been devised in a manner influenced by gender beliefs. Because of a preexisting belief that men had better spatial skills than women, some test creators “selectively bred” spatial instruments to produce the expected gender differences. Such instruments, including the very popular Mental Rotation Test (MRT), cannot validly assess between-group differences. Biological or evolutionary explanations for sex differences in spatial ability lack empirical evidence. Instead, the differences are rooted in the shaping of the construct of “spatial ability” to create the expected gender patterns. </p> <p><br></p> <p>Finally, I describe an experiment designed to investigate the hypothesis that using a spatial test with content from a feminized discipline will show different patterns in gender differences. Female engineering students outperformed males on the Digital Apparel Spatial Visualization Test (DASVT), while the male engineering students scored higher than the female students on the Purdue Spatial Visualization Test (PSVT:R). Students with relevant background experience scored better than students without experience on both assessments. The results demonstrate the shortcomings of using a single instrument to assess a concept as heterogeneous as spatial skills. I conclude this dissertation with a discussion of the implications of my work and recommendations for researchers and educators. </p>

<p>  </p> <p>Food system STEM projects have the capacity to motivate high school students in urban schools. This study explored food as a context to engage students because everyone interacts with food on a daily basis and has had cultural experiences related to food. An integrated STEM approach in combination with a systems thinking approach challenged students to make transdisciplinary connections, view problems from different perspectives, analyze complex relationships, and develop 21st-century and career skills (Hilimire et al., 2014; Nanayakkara et al., 2017). The purpose of this study was to describe and explain the relevance students perceive in Ag+STEM content by measuring high school students' self-efficacy, intrinsic value, attainment value, cost value, and utility value after participating in a food system STEM project. The study was informed by Eccles and Wigfield’s (2020) Situated Expectancy Value Theory. The convenience sample of this study was comprised of high school students from metropolitan area schools. High school students completed a food system STEM project with a food system context. Quantitative data was collected using the developed Food System Motivation questionnaire. Data were collected through a retrospective pre-test and a post-test. Descriptive statistics were used to analyze the data including means and standard deviations. Relationships were explored by calculating correlations.</p> <p>There were four conclusions from this study. First, high school students were somewhat interested, felt it was important to do well, and agreed there were costs regarding participation in the food system STEM project. Second, high school students reported higher personal and local utility value motivation after completing the food system STEM project. Third, high school students were somewhat self-efficacious in completing the project tasks and completing the project tasks informed by their cultural identity and experiences. Fourth, intrinsic value and attainment value motivation (independent variables) were related to personal and local utility value motivation and project and cultural self-efficacy motivation (dependent variables). Implications for practice and recommendations for future research were discussed.</p>

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<p> The limited numbers of women in advanced mathematics courses is a critical factor hindering women’s academic and professional access to science, technology, engineering, and mathematics (STEM) fields. Informal learning environments have the potential to play a significant role in promoting the participation of girls and women in mathematics/STEM fields. However, research that addresses the intersection of informal education, mathematics education, and women’s studies is minimal. Specifically, little is known about informal educators’ lived experiences in facilitating girls’ learning. Based on four years of working alongside Laura, the founder of Girls Excelling in Math and Science (GEMS) clubs, I conducted a narrative inquiry that explored our boundary crossing experiences as we engaged in a GEMS collaboration. The exploration focused on Laura’s narratives of her past, present, and future experiences that shape her identity as an informal educator. During the exploration of Laura’s experiences, I reflected on and inquired about my own personal and professional experiences across multiple contexts that inform my evolving identity as an educator. The theoretical framework of this study is informed by feminist theory and boundary-crossing perspectives. Feminist theory guides me to perceive our narrative of experiences from a women’s perspective while the boundary-crossing framework provides an analytic lens to understand our interpersonal and intrapersonal boundary crossing experiences. Because of the nature of the narrative inquiry, data were co-constructed between Laura and me in various forms: interviews, field notes, family stories, autobiographical writing, documents, conversations, emails, etc. I employed Polkinghorne’s (1995) <em>narrative analysis </em>and <em>analysis of narrative</em> approaches to analyze data. First, I utilized a <em>narrative analysis </em>approach to generate three holistic plots: (1) narratives of becoming female educators, (2) boundary-crossing collaboration in the midst of GEMS, and (3) conceptualizing mathematics across multiple contexts. An<em> analysis of narrative</em> approach was used to generate themes that unfold the meanings of stories, moments, and events and configure the plot. In the findings, I portrayed the three plots which allowed me to rediscover and reconstruct our personal practical knowledge across the contexts. Building on the findings, I discuss how female educators’ narratives of experiences inform their personal practical knowledge, which empowers girls’ and women’s personal and social experiences in mathematics/STEM. Laura and I cross multiple boundaries engaging in collaboration which provides an example of the boundary crossing collaboration between mathematics education and informal education. Based on the findings, I describe how informal learning STEM environments provide potential spaces to implement alternative curricula to humanize mathematics. Two evolving mathematics-related tasks illustrate our experiences of humanizing mathematics in GEMS. This study is situated at the intersection of mathematics education, informal education, and women’s studies, which significantly impacted Laura, myself, and GEMS, the context in which this study took place. This study provides an example of the possibilities of building boundary-crossing collaborations between the mathematics education community and the informal education community to empower girls and women in mathematics/STEM. Drawing on this dissertation study, one future research direction focuses on implementing and further developing humanized mathematics curricula in informal learning environments. Another research direction is using intersectional feminist theory to understand women’s differences regarding multiple social constructs (e.g., race, gender, class, ethnicity) to explicate the dimensions of inequality women face in mathematics/STEM. The study also suggests future practical work for mathematics education to foster alternative ways of conceptualizing mathematics regarding curriculum and approach. Mathematics educators could contribute to creating a learning community and providing professional development opportunities to support informal educators. </p>

Attempts to improve retention, interest, and enrollment of females in Technology & Engineering Education courses have included a variety of approaches including female-only classes. However, the implications of such courses have not been thoroughly investigated. Therefore, an investigation of female-only classes was undertaken; the findings revealed that the overall enrollment of females went up in the course and in subsequent classes, these students maintained their interests and attitudes towards Technology and Engineering, their perceptions of an engineer's gender changed from that of mostly male to mostly female, and their concepts of what an engineer does changed from mostly building or fixing things to that of mostly someone who designs. This study used two instruments: the Technology Engineering Attitude Survey (TEAS) and the Draw an Engineer Test (DAET). The population of the study was 7th grade middle school students. They were placed into two groups: the control being the mixed male female engineering and technology classes, and the treatment being the all-female students enrolled in the same engineering technology course.

This is a project that explores the realm of programming and art education for young children, trying to bring these two disciplines together in a fun and playful way. The aim is to allow children to express themselves creatively and at the same time explore and understand basic concepts of coding and programming. From the idea of creating a tool for education, this project has evolved, throughout the iterative design process of prototyping and testing, into a toolbox for personal exploration and discovery of Art and technology. This toolbox not only aims to give children the opportunity to grow and develop their understanding of digital media but also aims to help them create stronger bonds with people around them, by sharing and participating together in the activities that they will create. Prototyping is here used as an explorative tool to not only develop the final design but to also investigate children, their interests and aspirations. The different iterations have helped getting a deeper understanding of the user and the concepts revolving around the topic of art and programming to which this project try to talk to. The outcome is a physical modular interface that allows children to build, piece by piece, a digital and interactive art experience that they can eventually share and play with family and friends.