This study attempts to analyze the impact of an online educational resource on academic performances among newly arrived immigrant students in Sweden between the grade six to nine in the Swedish school system. The study focuses on the web based educational resource called Studi.se made by Komplementskolan AB.The aim of the study was to investigate the relationship between academic performance and using Studi.se. Another purpose was to see what other factors that can impact academic performances.The study made use of the data mining process, Cross Industry Standard for Data Mining (CRISP-DM), to understand and prepare the data and then create a regression model that is evaluated. The regression model tries predict the dependent variable of grade based on the independent variables of Studi.se activity, gender and years in Swedish schools. The used data set includes the grades in mathematics, physics, chemistry, biology and religion of newly arrived students in Sweden from six municipalities that have access to Studi.se. The data used also includes metrics of the student’s activity on Studi.se.The results show negative correlation between grade and gender of the student across all subjects. In this report, the negative correlation means that female students perform better than male students. Furthermore, there was a positive correlation between number of years a student has been in the same school and their academic grade. The study could not conclude a statistically significant relationship between the activity on Studi.se and the students’ academic grade.Additional explanatory independent variables are needed to make a predictive model as well as investigating alternative regression models other than multiple linear regression. In the sample, a majority of the students have little or no activity on Studi.se despite having free access to the resource through the municipality. / Denna studie analyserar inverkan som digitala läromedel har på skolbetyg bland nyanlända elever i Sverige mellan årskurs sex och nio i det svenska skolsystemet. Studien fokuserar på den webbaserade pedagogisk resursen Studi.se, gjord av Komplementskolan AB.Målet med studien var att undersöka relationen mellan skolresultat och användandet av Studi.se. Ett annat syfte var att undersöka vad för andra faktorer som kan påverka skolresultat.Studien använder sig av datautvinningsprocessen, Cross Industry Standard for Datamining (CRISP-DM), för att förstå, förbereda och analysera datan i form av en regressionsmodell som sedan evalueras. Datasamlingen som används innehåller bland annat skolbetyg i ämnena matematik, fysik, kemi, biologi och religion från sex kommuner som har tillgång till Studi.se. Aktivitet hos eleverna från dessa kommuner på Studi.se hemsidan användes också för studien.Resultaten visar en negativ korrelation mellan betyg och kön hos eleverna i alla ämnena. Den negativa korrelationen betyder i denna rapport att tjejer får bättre betyg i genomsnitt än killar hos urvalet av nyanlända från de sex kommunerna. Dessutom fanns det en positiv korrelation mellan antal år en elev varit i skolan alternativt i svenska skolsystemet och deras betyg. Studien kunde inte säkerställa ett statistisk signifikant resultat mellan aktivitet på Studi.se och elevernas skolresultat.Ett flertal förklarande oberoende variabler behövs för att kunna skapa en prognastisk modell för skolresultat samt bör en undersökning på alternativa regressions modeller förutom linjär multipel regression göras. I studiens urval av nyanlända elever från kommunerna, har majoriteten inte använt eller knappt använt Studi.se även om dessa kommuner haft tillgång till denna resurs.

Despite widespread recognition by science educators, researchers and K-12 frameworks that scientific inquiry should be an essential part of science education, typical classrooms and assessments still emphasize rote vocabulary, facts, and formulas. One of several reasons for this is that the rigorous assessment of complex inquiry skills is still in its infancy. Though progress has been made, there are still many challenges that hinder inquiry from being assessed in a meaningful, scalable, reliable and timely manner. To address some of these challenges and to realize the possibility of formative assessment of inquiry, we describe a novel approach for evaluating, tracking, and scaffolding inquiry process skills. These skills are demonstrated as students experiment with computer-based simulations. In this work, we focus on two skills related to data collection, designing controlled experiments and testing stated hypotheses. Central to this approach is the use and extension of techniques developed in the Intelligent Tutoring Systems and Educational Data Mining communities to handle the variety of ways in which students can demonstrate skills. To evaluate students' skills, we iteratively developed data-mined models (detectors) that can discern when students test their articulated hypotheses and design controlled experiments. To aggregate and track students' developing latent skill across activities, we use and extend the Bayesian Knowledge-Tracing framework (Corbett & Anderson, 1995). As part of this work, we directly address the scalability and reliability of these models' predictions because we tested how well they predict for student data not used to build them. When doing so, we found that these models demonstrate the potential to scale because they can correctly evaluate and track students' inquiry skills. The ability to evaluate students' inquiry also enables the system to provide automated, individualized feedback to students as they experiment. As part of this work, we also describe an approach to provide such scaffolding to students. We also tested the efficacy of these scaffolds by conducting a study to determine how scaffolding impacts acquisition and transfer of skill across science topics. When doing so, we found that students who received scaffolding versus students who did not were better able to acquire skills in the topic in which they practiced, and also transfer skills to a second topic when was scaffolding removed. Our overall findings suggest that computer-based simulations augmented with real-time feedback can be used to reliably measure the inquiry skills of interest and can help students learn how to demonstrate these skills. As such, our assessment approach and system as a whole shows promise as a way to formatively assess students' inquiry.

Despite widespread recognition by science educators, researchers and K-12 frameworks that scientific inquiry should be an essential part of science education, typical classrooms and assessments still emphasize rote vocabulary, facts, and formulas. One of several reasons for this is that the rigorous assessment of complex inquiry skills is still in its infancy. Though progress has been made, there are still many challenges that hinder inquiry from being assessed in a meaningful, scalable, reliable and timely manner. To address some of these challenges and to realize the possibility of formative assessment of inquiry, we describe a novel approach for evaluating, tracking, and scaffolding inquiry process skills. These skills are demonstrated as students experiment with computer-based simulations. In this work, we focus on two skills related to data collection, designing controlled experiments and testing stated hypotheses. Central to this approach is the use and extension of techniques developed in the Intelligent Tutoring Systems and Educational Data Mining communities to handle the variety of ways in which students can demonstrate skills. To evaluate students' skills, we iteratively developed data-mined models (detectors) that can discern when students test their articulated hypotheses and design controlled experiments. To aggregate and track students' developing latent skill across activities, we use and extend the Bayesian Knowledge-Tracing framework (Corbett & Anderson, 1995). As part of this work, we directly address the scalability and reliability of these models' predictions because we tested how well they predict for student data not used to build them. When doing so, we found that these models demonstrate the potential to scale because they can correctly evaluate and track students' inquiry skills. The ability to evaluate students' inquiry also enables the system to provide automated, individualized feedback to students as they experiment. As part of this work, we also describe an approach to provide such scaffolding to students. We also tested the efficacy of these scaffolds by conducting a study to determine how scaffolding impacts acquisition and transfer of skill across science topics. When doing so, we found that students who received scaffolding versus students who did not were better able to acquire skills in the topic in which they practiced, and also transfer skills to a second topic when was scaffolding removed. Our overall findings suggest that computer-based simulations augmented with real-time feedback can be used to reliably measure the inquiry skills of interest and can help students learn how to demonstrate these skills. As such, our assessment approach and system as a whole shows promise as a way to formatively assess students' inquiry.