Successful Integration of Refugee Students in Higher Education: Insights from Entry Diagnostics in an Online Study Program

Accessing higher education without having to overcome bureaucratic hurdles is a serious concern for refugees. Although empirical studies on the integration and success of refugees in higher education are scarce, the challenges related to this issue are becoming apparent. The Success and Opportunities for Refugees in Higher Education (SUCCESS) research project has been launched to investigate the effectiveness of new online study programs offered on the Kiron Open Higher Education (Kiron) platform that provides refugees with access to Massive Open Online Courses (MOOCs). SUCCESS measures the prior knowledge and skills of refugee students and investigates to what extent their study opportunities, learning processes, and chances of academic success can be improved effectively through different forms of support provided in Kiron. In this paper, we present the assessment framework and study design of the SUCCESS project as well as data on 1,376 students entering the study program in Kiron in summer 2017. As students’ language skills, intellectual abilities, and prior study-related knowledge play a significant role in their performance in higher education degree programs, we focus on the crucial introductory study phase and valid diagnostics of students’ study preconditions. We analyze refugee students’ socio-biographical and educational data such as gender, country of origin, highest level of education achieved etc. and examine their English language skills, intellectual abilities, and previous study domain related knowledge. We find extreme differences in levels of education and preconditions on starting to study in Kiron. Based on these results, we discuss implications for the effective and successful integration of refugee students in higher education. In this paper, we present the evidence-based model, assessment framework, and study design of the XXXXX project, as well as data on 1,376 students entering a study program through Kiron in summer 2017. Because students’ language skills, intellectual abilities, and prior study-related knowledge play a significant role in their performance in higher education degree programs, we focus on the crucial introductory study phase and valid diagnostics of students’ study preconditions. We analyze refugee students’ socio-biographical and educational data such as gender, country of origin, highest level of education achieved etc. and examine their English language skills, intellectual abilities, and previous study domain related knowledge. We find extreme differences in levels of education and preconditions on starting to study through Kiron. Based on these results, we discuss implications for the effective and successful integration of refugee students in higher education

Applying psychometric modeling to aid feature engineering in predictive log-data analytics. The NAEP EDM Competition

The NAEP EDM Competition required participants to predict efficient test-taking behavior based on log data. This paper describes our top-down approach for engineering features by means of psychometric modeling, aiming at machine learning for the predictive classification task. For feature engineering, we employed, among others, the Log-Normal Response Time Model for estimating latent person speed, and the Generalized Partial Credit Model for estimating latent person ability. Additionally, we adopted an n-gram feature approach for event sequences. Furthermore, instead of using the provided binary target label, we distinguished inefficient test takers who were going too fast and those who were going too slow for training a multi-label classifier. Our best-performing ensemble classifier comprised three sets of low-dimensional classifiers, dominated by test-taker speed. While our classifier reached moderate performance, relative to the competition leaderboard, our approach makes two important contributions. First, we show how classifiers that contain features engineered through literature-derived domain knowledge can provide meaningful predictions if results can be contextualized to test administrators who wish to intervene or take action. Second, our re-engineering of test scores enabled us to incorporate person ability into the models. However, ability was hardly predictive of efficient behavior, leading to the conclusion that the target label\u27s validity needs to be questioned. Beyond competition-related findings, we furthermore report a state sequence analysis for demonstrating the viability of the employed tools. The latter yielded four different test-taking types that described distinctive differences between test takers, providing relevant implications for assessment practice. (DIPF/Orig.

Rapid guessing rates across administration mode and test setting

Rapid guessing can threaten measurement invariance and the validity of large-scale assessments, which are often conducted under low-stakes conditions. Comparing measures collected under different administration modes or in different test settings necessitates that rapid guessing rates also be comparable. Response time thresholds can be used to identify rapid guessing behavior. Using data from an experiment embedded in an assessment of university students as part of the National Educational Panel Study (NEPS), we show that rapid guessing rates can differ across modes. Specifically, rapid guessing rates are found to be higher for un-proctored individual online assessment. It is also shown that rapid guessing rates differ across different groups of students and are related to properties of the test design. No relationship between dropout behavior and rapid guessing rates was found. (DIPF/Orig.