THIS EVENT HAS BEEN CANCELLED. APOLOGIES FOR ANY INCONVENIENCE CAUSED.
According to generalized internal/ external (GI/E) frame-of-reference model, motivational beliefs are explained through academic achievement. In Africa respective studies are rare. In the present study, we investigated the model’s applicability to expectancy, utility, and cost beliefs of Rwandan lower-secondary students (N = 771; 51.0% female) within Chemistry and Math (quantitative domain) as well as English and Kinyarwanda (language domain). Through multiple-group structural equation models (SEM) we compared the model’s applicability to basic-education and boarding schools. Admission to boarding schools depends amongst others on performance during national school examinations. Hence, both school types can be interpreted as different tracks within Rwanda’s system of school-level ability grouping of students. The model’s applicability differed across school types. Within basic-education schools, achievement predicted mainly cost beliefs. Within boarding schools, achievement predicted cost and especially expectancy beliefs. Across both types, respective beliefs were positively predicted by achievement within subjects. Within basic-education schools, beliefs within one language were also positively predicted by achievement in the other language (i.e., assimilation effects). Within boarding schools, beliefs within subjects of one of the domains (i.e., language or quantitative) were negatively predicted by prior achievement in subjects of the other domain (i.e., contrast effects). We therefore concluded that school-level contextual factors such as multilingualism may moderate motivational processes that Rwandan secondary students experience. This may have implications especially for the design of motivational interventions whose potential has not been fully explored within the African context.
Traditional invariance testing via multiple group CFA is of limited utility when the number of groups is not small. Alignment optimization was recently developed to address this practical issue when the number of groups is not small (see Muthen & Asparouhov, 2013, 2014). This talk introduces alignment optimization and its utility in educational measurement invariance testing. A comparison with the traditional approach and examples of applications from recently published research are provided. Finally, a new measurement invariance study of intersectional groupings (ethnicity, gender, SES) and two longitudinally cohorts of several self-efficacy scales is presented, including discussions of the substantive findings, technical issues, and future directions.
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Objectives: Educational resilience is the exhibition of positive educational experiences and outcomes despite exposure to risk. It is also the product of a multidimensional interaction between the child and their immediate environment. There is a growing number of unaccompanied refugee minors worldwide seeking asylum and protection. In response, education systems in host countries must deepen their knowledge and engagement with the needs and circumstances of unaccompanied refugee minors. There is still little empirical evidence on the educational resilience of unaccompanied refugee minors.
Methods: The study analyzed the reading skills outcomes of 410 Palestinian refugee minors enrolled at UNRWA (United Nations Relief and Works Agency) schools in Jordan at age 15 in 2009 (91 of whom are unaccompanied). Using stepwise multilevel regression, this study sought to identify student-level and school-level factors that function as educational resilience correlates for Palestinian refugee minors in Jordan.
Findings: Young age, female gender, high socio-economic status, positive teacher-student relations, and exposure to structuring and scaffolding strategies were associated with higher reading skills among Palestinian unaccompanied refugee minors. Educational and school-based interventions and programs need further elaboration to account for educational resilience correlates specific to this population.
All welcome to join in person.
If you wish to join online, pre-registration is required (no need to register again if you have already done so in a previous week of Trinity Term) Register to join this event via Zoom
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All welcome to join in person.
If you wish to join online, pre-registration is required (no need to register again if you have already done so in a previous week of Trinity Term): Register to join this event online via Zoom
Once your registration has been approved, you will receive a confirmation email with joining instructions.
Abstract
The universalisation of school education in India has altered the playing field, changing the expectations placed on teachers and schools to provide ‘quality’ teaching in the face of increasing diversification of the student population, growing privatisation of schooling, and what is frequently termed a ‘learning crisis’. In this context, this presentation explores some of the trends in school and teacher performance and classroom practices in two states in Southern India, using multilevel analysis of quantitative school survey data from the Young Lives study.
Policymakers, conceptualized here as principals, disagree as to whether US student performance has changed over the past half century. To inform conversations, agents administered seven million psychometrically linked tests in math (m) and reading (rd) in 160 survey waves to national probability samples of cohorts born between 1954 and 2007. Estimated change in standard deviations (sd) per decade varies by agent (m: –0.10sd to 0.27sd, rd: –0.02sd to 0.12sd). Consistent with Flynn effects, median trends show larger gains in m (0.19sd) than in rd (0.04sd), though rates of progress for cohorts born since 1990 have increased in rd but slowed in m. Greater progress is shown by students tested at younger ages (m: 0.31sd, rd: 0.08sd) than when tested in middle years of schooling (m: 0.17sd, rd: 0.03sd) or toward the end of schooling (m: 0.06sd, rd: 0.02sd). Young white students progress more slowly (m: 0.28sd, rd: 0.09sd) than Asian (m: 46sd, rd: 0.28sd), black (m: 0.36sd, rd: 0.19sd), and Hispanic (m: 0.29sd, rd: 0.13sd) students. These ethnic differences generally attenuate as students age. Young students in the bottom quartile of the SES distribution show greater progress than those in the top quartile (difference in m: 0.08sd, in rd: 0.15sd), but the reverse is true for older students. Moderators likely include not only changes in families and schools but also improvements in nutrition, health care, and protection from contagious diseases and environmental risks. International data suggest that subject and age differentials may be due to moderators more general than just the United States.
Read more: Shakeel, M.D. & Peterson, P.E. (2022). A Half Century of Progress in US Student Achievement: Agency and Flynn Effects, Ethnic and SES Differences. Educational Psychology Review.
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There is large interest in intensive longitudinal data analysis in the social, educational and health sciences. Datasets can include (1) self-reports or multiple-reporter data (e.g., observed on-task behaviour, self-reported situation-specific competence) collected using diaries, experience sampling, or ecological momentary assessments, (2) task-data (e.g., trace-data, executive functioning), (3) real-time ambulatory data (e.g., accelerometer, electrodermal activity, eye-tracking), or mixtures of these. In the talk I will focus on challenges researchers face when they (i) handle and aggregate data, (ii) consider the time-structure for analysis, and (iii) specify statistical models. Time-series-based Dynamic Structural Equation Models (DSEM) using the Bayesian estimator are emerging, allowing researchers to switch focus from modelling fixed and random effects, to modelling individual processes over time. In the talk, I will illustrate intensive longitudinal data handling and modelling with on-going research, in order to highlight its relevance for understanding intraindividual processes in educational research.
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Over the past five years, the broader field of Natural Language Processing (NLP) has undergone a renaissance, driven largely by the emergence of pre-trained, word-embedding-based language models such as BERT and GPT-3, resulting in significant improvement in a variety of core NLP challenges such as sentiment analysis, machine translation, and transcription, the latter two of which have reached human-level performance. While educational applications of NLP have been a topic of research for decades, the limitations of previous NLP techniques had meant that most successful applications had been restricted to narrow domains. However, recent advances in NLP mean that challenges that had been considered prohibitively complex such as interactive chatbots, speech recognition, or automatic grading of complex open-ended responses, may now be tractable.
My specific focus of research is on the potential of NLP to assist in the formative assessment of basic literacy in low-and-middle-income countries (LMICs). In many LMICs, it is challenging to conduct high-quality, formative assessments of children’s literacy due to a variety of factors. As a result, large-scale standardized assessments, which typically consist of silently reading passages and then answering multiple-choice questions is become the de-facto method for nations to assess students’ literacy. This is a problem both because the assessment format is poorly suited for assessing basic literacy, and because the assessments are conducted infrequently and on a small sample of students, meaning the results cannot be used at the classroom level to improve instruction. In the past, more effective approaches to formative literacy assessment (e.g., oral reading, story-retell, short-answer questions), were rarely used because they were substantially more difficult and time-consuming to administer and grade.
However, given the recent advances in NLP and the proliferation of publicly available pre-trained language models, it appears feasible to partially automate the administration and scoring of formative literacy assessment. To test this, I am collaborating with a school network in Ghana to conduct a series of literacy assessments with approximately 500 of their primary school students. Students’ responses will be graded by a mix of experts and crowd workers and will be used to train language models to score student responses similar to how would human raters. The results can be used in conjunction with the school network’s pre-existing reading achievement and student demographic data to investigate both the predictive and convergent validity of open-ended questions compared to traditional measures of reading ability, as well the models’ performance relative to human raters.
Anticipated Agenda
Recent advances in Natural Language Processing (20 min)
Implications for and potential applications in Education (20 min)
NLP and formative literacy assessment: current research and initial findings (30 min)
Questions/discussion (20 min)
The Quantitative Methods Hub at the Department of Education, University of Oxford, launches its 2022 Advanced Quantitative Methods Summer School, this year consisting of eight different online courses running during three calendar weeks in May (weeks 2, 3 and 4 of Trinity Term; 5-20/5/2022).
The courses require a basic understanding of multiple regression modelling or other multivariate techniques. Students, staff and professionals are welcome to attend one, some or all days by signing up via the online store.
The courses will take place online, and include a mixture of pre-recorded lectures and demonstrations, synchronous lectures, workshops and questions & answer sessions. Synchronous session sessions will run in zoom. Links to materials and zoom-sessions will be sent to participants. You will use R-studio and various R-modules, and Mplus (Mplus demo) during the courses.
Download our course overview to find out more, or get in touch with us.
Cost per course:
- £25 for OU and Grand Union students (£60 for a week)
- £100 for staff and external students (£250 for a week)
- £200 for professionals (£450 for a week).
Sign up for the Summer School at the online store
Programme Structure
- Oxford Week 2 (online)
Thu 5/5 Thees Spreckelsen Getting up and running in R
Fri 6/5 Thees Spreckelsen Data management and documenting in R
- Oxford Week 3 (online)
Mon 9/5 Rees van de Schoot & Laura Hofstee AI-supported systematic reviews
Tue -Wed 10-11/5 Kit Double Multilevel modelling (parts 1 and 2)
Fri 13/5 Luning Sun Intro to IRT modelling
- Oxford Week 4 (face-to-face & online)
Mon 16/5 Lars Malmberg Intro Structural Equation Models (SEM)
Wed 18/5 Lars Malmberg Longitudinal SEM
Fri 20/5 Lars Malmberg Multilevel and intraindividual SEM
The Quantitative Methods Hub at the Department of Education, University of Oxford, offers its 2022 Advanced Quantitative Methods Summer School, this year consisting of eight different online courses running during three calendar weeks in May (weeks 2, 3 and 4 of Trinity Term; 5-20/5/2022).
The courses require a basic understanding of multiple regression modelling or other multivariate techniques. Students, staff and professionals are welcome to attend one, some or all days by signing up via the online store.
The courses will take place online, and include a mixture of pre-recorded lectures and demonstrations, synchronous lectures, workshops and questions & answer sessions. Synchronous session sessions will run in zoom. Links to materials and zoom-sessions will be sent to participants. You will use R-studio and various R-modules, and Mplus (Mplus demo) during the courses.
Download our course overview to find out more, or get in touch with us.
Cost per course:
- £25 for OU and Grand Union students (£60 for a week)
- £100 for staff and external students (£250 for a week)
- £200 for professionals (£450 for a week).
Sign up for the Summer School at the online store
Programme Structure:
- Oxford Week 2 (online)
Thu 5/5 Thees Spreckelsen Getting up and running in R
Fri 6/5 Thees Spreckelsen Data management and documenting in R
- Oxford Week 3 (online)
Mon 9/5 Rees van de Schoot & Laura Hofstee AI-supported systematic reviews
Tue -Wed 10-11/5 Kit Double Multilevel modelling (parts 1 and 2)
Fri 13/5 Luning Sun Intro to IRT modelling
- Oxford Week 4 (face-to-face & online)
Mon 16/5 Lars Malmberg Intro Structural Equation Models (SEM)
Wed 18/5 Lars Malmberg Longitudinal SEM
Fri 20/5 Lars Malmberg Multilevel and intraindividual SEM