Software is regarded as a promising means to innovate education, particularly in light of a widely occurring transition from instructor-centered to student-centered instructional approaches. However, with a variously understood notion of student-centeredness and disjointed efforts to study the effects of technology in education, evaluating how technology may help students study a language in their preferred ways to chase their individual interests has remained subject to rigorous inquiry. This thesis takes the study of Japanese as a foreign language (L2) in Dutch higher education as a case in point to investigate through grounded design how technology can facilitate student-personalized learning. To this end, literature reviews were conducted on the topics of human learning, technology in language education, and L2 Japanese-language education. A focus group study was moreover performed with three university instructors of L2 Japanese, as well as an evaluation of a software prototype, Kamo, with students of L2 Japanese, to situate the theoretical findings in practice. Strong potentials were identified for software to arrange more individually attuned study materials and help students find peers with similar interests, under the condition that software complements rather than substitutes face-to-face instruction. A key requirement for personalizing technology to effectively integrate into language education is that the curriculum is adjusted to provide sufficient time and technical support to find and use adequate tools. Future research is recommended to take a larger-scale approach so as to prevent the recurring issue of non-generalizable outcomes and to define joint study objectives that can utilize the ever-changing technological landscape in a sustainable way. ... Read more

Rotterdam Werkt! is a network of fourteen organizations in the Rotterdam area in the Netherlands. Their goal is to increase labor mobility between these organizations through sharing vacancies, exchanging employees and partaking in joint projects. Rotterdam Werkt! has tasked us with creating a central platform on which all vacancies are automatically combined from the websites of all the organizations in the network. The two main challenges of the project were to gather the vacancies from all the organizations affiliated with Rotterdam Werkt! and allow their recruiters to search and filter through them. This meant that a significant amount of research needed to be done in order to find a suitable scraping tool as well as a suitable search engine. Whilst gathering the vacancies, we ran into the problem that each website was significantly different in the way it is rendered. Furthermore, we also needed to categorize the data correctly such that it becomes searchable in the search engine. Lastly, the retrieval function needed to be optimized such that the most relevant vacancies would be returned for a given query. In order to assess whether recruiters could use the search engine effectively in practice, an evaluation of the effectiveness of the search engine was done. Three retrieval functions were compared based on a significance test of several effectiveness measures that indicate to what extent a retrieval function is able to retrieve relevant documents, or in this case, vacancies. Out of the three, the retrieval function that scored the highest was chosen to be used in the platform, so that recruiters will have a bigger chance to find the vacancies they will be looking for. In the end, we consider our project to be a success. We managed to scrape all vacancies from all the websites of the organizations in Rotterdam Werkt! and to combine these on a centralized platform. Furthermore, the search engine evaluation allowed us to select the best vacancy retrieval function out of the three evaluated retrieval functions. However, more work can still be put into evaluating the search engine in the future by testing more retrieval functions based on more vacancy data, so that the search functionality can be further improved.
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