Esta investigación es una revisión y análisis documental que permitió reflexionar en torno a los desafíos culturales, tecnológicos y curriculares que la educación STEM1 presenta en Occidente, específicamente en la formación primaria. Así mismo, se quiere abrir nuevas líneas de indagación en torno a un marco conceptual para lo que sería un análisis más detallado y minucioso sobre la temática estudiada. Esta propuesta es creada desde la iniciativa de enseñar las ciencias y matemáticas desde un aprendizaje activo; donde se busca que los estudiantes puedan realizar algunas indagaciones científicas y hacer uso de la tecnología como un recurso conveniente en las experiencias de aprendizaje. En la investigación se podrán observar las aproximaciones al concepto de STEM, así como los desafíos y algunas experiencias de aplicación en docentes del grado primaria. / This research is a documentary review and analysis that allowed us to reflect on the cultural, technological and curricular challenges that STEM education presents in the West, specifically in primary education. Likewise, it wants to open new lines of inquiry around a conceptual framework for what would be a more detailed and thorough analysis of the subject matter studied. This proposal is created from the initiative of teaching science and mathematics from active learning; where it is sought that students can carry out some scientific inquiries and make use of technology as a convenient resource in learning experiences. In the investigation, the approaches to the STEM concept will be observed, as well as the challenges and some experiences of application in primary grade teachers. / Trabajo de investigación

No description available.

No description available.

<div> <div> <div> <p>Previous work has identified the many difficulties that students experience in learning abstract concepts in STEM. Past studies have also identified the critical role that emotions play on students' motivation to learn. As new learning technologies are developed, they enable visualizing complex scientific concepts which can be non-visible thus assisting students' understanding of abstract ideas as well as improving their motivation as they learn. This study investigated two learning technologies and compared them to examine 1) their effectiveness on learning concepts of electricity in physics and 2) the interplay between learning with technology and emotions. Participants were randomly assigned to either Inquiry-Based Learning (IBL) with a computer simulation or Game-Based Learning (GBL) with a computer game which addressed concepts of electricity in physics. During the experiment, students in the IBL condition explored materials by using the computer simulation and posed hypotheses and questions on their own with a guiding worksheet for IBL. Students in the GBL condition played an educational computer game following the guiding worksheet while they were meeting challenges created by the game with a guiding worksheet for GBL. Students' learning gains were assessed by comparing their pretest and posttest scores. Emotions were self-reported after the posttest by responding to a survey that measured 6 emotional scales that students may perceive during the experiment. The study found that both IBL and GBL enhanced students' understanding of given concepts. However, there was no statistically significant difference between the two conditions in terms of learning gains. Students in the IBL achieved higher mean learning gains, whereas students in the GBL showed that they were more engaged. At the same time, students in the GBL perceived more confusion and frustration compared to students in the IBL. <br></p> </div> </div> </div>

No description available.

No description available.

No description available.

No description available.

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>