Going the distance: The effects of university – secondary school collaboration on student migration

Raab, Jörg; Knoben, Joris; Aufurth, Ludmilla; Kaashoek, Bram

doi:10.1111/pirs.12288

Cited by 6 publications

(1 citation statement)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Clark and Lovric (2009) define a transition to be successful when the student is comfortable in her/his new role as university student, is able to achieve and work towards her/his goals, shows good academic progress, has support (both academic and otherwise) and can access it when needed, and enjoys the new courses. Rach and Heinze (2017) show that specific precursory knowledge related to scientific mathematics, and students' abilities to develop adequate learning strategies are the main factors for a successful transition phase. Another issue in the transition from secondary to tertiary education is whether students make the right choices: does the new institution or programme correspond to the perception of the student upon entering?…”

Section: Introductionmentioning

confidence: 98%

High-school students engaging with researchers within a pre-university programme: Motivations and experiences

Michels¹,

Eijkelhof²

2018

Research for All

View full text Add to dashboard Cite

For students, the transition between secondary school and higher education can be problematic. Their prior knowledge may be insufficient, they may lack the right attitude or not have enough skills for university. Especially gifted students often lack challenges to remain motivated. Moreover, it is not easy for secondary school students to get a good picture of a variety of further studies. For their teachers, it is difficult to keep students informed about actual research in science and technology, as they are not in touch with this research on a regular basis. In this paper, we report about experiences of more than ten years at Utrecht University, offering pre-university students opportunities to be involved in lectures, workshops, laboratories and research at the university. We report on students' motivation to register for this programme, and their experiences afterwards. Important categories of motivation and experience are: raising interest in science; the choice of, and preparation for, further studies; working with a group of similar, motivated students; the wish to be challenged; and the experience of doing research at the university. University lecturers involved in the programme like to work with these students, and are motivated to share their research experiences.

show abstract

Section: Introductionmentioning

confidence: 98%

High-school students engaging with researchers within a pre-university programme: Motivations and experiences

Michels¹,

Eijkelhof²

2018

Research for All

View full text Add to dashboard Cite

show abstract

Spatial competition for students: What does (not) matter?

Lourenço

Sá

2019

Ann Reg Sci

View full text Add to dashboard Cite

Forecasting first-year student mobility using explainable machine learning techniques

Litmeyer,

Hennemann

2024

Rev Reg Res

View full text Add to dashboard Cite

In the context of regional sciences and migration studies, gravity and radiation models are typically used to estimate human spatial mobility of all kinds. These formal models are incorporated as part of regression models along with co-variates, to better represent regional specific aspects. Often, the correlations between dependent and independent variables are of non-linear type and follow complex spatial interactions and multicollinearity. To address some of the model-related obstacles and to arrive at better predictions, we introduce machine learning algorithm class XGBoost to the estimation of spatial interactions and provide useful statistics and visual representations for the model evaluation and the evaluation and interpretation of the independent variables. The methods suggested are used to study the case of the spatial mobility of high-school graduates to the enrolment in higher education institutions in Germany at the county-level. We show that machine learning techniques can deliver explainable results that compare to traditional regression modeling. In addition to typically high model fits, variable-based indicators such as the Shapley Additive Explanations value (SHAP) provide significant additional information on the differentiated and non-linear effect of the variable values. For instance, we provide evidence that the initial study location choice is not related to the quality of local labor-markets in general, as there are both, strong positive and strong negative effects of the local academic employment rates on the migration decision. When controlling for about 28 co-variates, the attractiveness of the study location itself is the most important single factor of influence, followed by the classical distance-related variables travel time (gravitation) and regional opportunities (radiation). We show that machine learning methods can be transparent, interpretable, and explainable, when employed with adequate domain-knowledge and flanked by additional calculations and visualizations related to the model evaluation.

show abstract

Going the distance: The effects of university – secondary school collaboration on student migration

Cited by 6 publications

References 37 publications

High-school students engaging with researchers within a pre-university programme: Motivations and experiences

High-school students engaging with researchers within a pre-university programme: Motivations and experiences

Spatial competition for students: What does (not) matter?

Forecasting first-year student mobility using explainable machine learning techniques

Contact Info

Product

Resources

About