Since its inception in 1995, JavaScript usage has grown far beyond its initial domain of interactive websites. As the size of applications developed in the language grows, so does the desire for static analysis such as typechecking to provide safety and reliability. Many developments have been made in recent years on increasing the precision of analysis of JavaScript. This work introduces a concrete and a type interpreter for LambdaJS implemented within the Sturdy framework. The goal of this work is to evaluate the feasibility of implementing a shared interpreter using the Sturdy framework, evaluate the correctness of the concrete interpreter, and evaluate the feasibility of proving the typechecker sound. The resulting interpreters are tested experimentally with two test suites. The experimental evaluation gives confidence in the correctness of the concrete and abstract interpreter. A small part of the abstract interpreter is proven sound to evaluate the difficultyofcreatingacompletesoundnessproof.Thesuccessful implementation of the interpreters shows that implementing a shared arrow-based interpreter within the Sturdy framework is feasible for languages with complex semantics, that the utilities available reduce the effort required to do so, and that proving the abstract interpreter sound is simplified by using the library.

Debuggers are crucial tools for developers to support the process of developing software systems as they provide direct insights into the execution of their code. As software development in the industry is moving towards technology stacks that operate on increasingly higher levels of abstraction, debugging tools have not evolved as quickly. This creates an abstraction gap between the concrete debugging needs of developers and the support that debugging tools offer. To reduce this gap, we propose a generic framework for reactive scriptable debugging. We propose a design and reference implementation of such a scriptable debugging system. Using this framework we aim to liberate developers from rigid debugging tools and give the power of debugging back to the developers.

Implementing the syntax and semantics of a new (domain-specific) programming language is a lot of work, which is worsened by the additional work needed to add support for the language to an editor such as Eclipse or VS Code. Lack of such support can impede language usability and adoption, as developers prefer different editors. However, supporting M editors for N languages requires M * N implementations to be built and maintained, which is known as the IDE portability problem. Portable editor services aim to reduce this to M + N implementations, which leads to the main question of this thesis: how can we make the editor services of languages portable across editors?
Language definitions made in the Spoofax language workbench can automatically expose their editor services in any editor that Spoofax supports. Therefore, we evaluate the portability of Spoofax Core, the editor-agnostic core of Spoofax, through an implementation of the workbench in the IntelliJ editor.
To get portability for editor services of languages in general, we first investigate how editor services can be added to the most popular editors, and explore their features, documentation, and API. From this, we derive a platform-agnostic model for portable editor services: AESI, the Adaptable Editor Services Interface. AESI describes the maximum set of common editor service features supported by the editors we investigated, while at the same time imposing minimal requirements upon any implementation of these editor services. We evaluate AESI by providing two language implementations, and adapting AESI to Eclipse, IntelliJ, and VS Code. Finally, we compare AESI with two other solutions to the IDE portability problem: LSP and Monto.

LeQuest develops interactive e-training modules to improve the competence regarding medical technology of medical professionals. The medical technology is analysed by LeQuest to develop training modules, but the analysis process and writing associated information can be performed more efficiently. This would reduce the required time and resources which could be invested in additional trainings and quality improvements. In the end, this will lead to an improvement regarding the patient's safety in health institutions. This work empirically evaluated the Spoofax Workbench by conducting an industrial case-study which consists of the design, implementation and evaluation of a domain-specific language (DSL). The LeQuest DSL is used as a tool for transforming the current analysis process into a more formalized process which does allow for objective observations, measurements and quantifiable information. Although the LeQuest DSL is not integrated in the current work-flow yet, the evaluation has shown that it is expected that the overall quality and efficiency of the analysis process will increase after the introduction of the DSL.

Batteryless network nodes could enable unprecedented ubiquitous sensing applications, while gathering energy from the environment and hence removing maintenance costs for batteries. However, these devices inherently suffer frequent power failures and reboots, due to the volatility of the incoming energy, thus classical synchronous message passing cannot work. On the other hand, the asynchronous scheme is not viable either, since power outages can occur at different rates on two devices, and for unpredictable lengths of time. In order to align wake-ups of wireless nodes, a non-canonical mechanism to keep track of time must be devised. We present a novel architecture for reliable packet exchange between energy-harvesting devices, resilient to power failures and agnostic about the physical layer. The proposed architecture, implemented on real hardware, shows an improvement in throughput of a factor of 10, and reduces energy waste by 33 % to 78 %, as compared to the asynchronous message passing.

This thesis improves sharing of code and reproducibility (S&R) in research for massive open online courses (MOOCs). Reproducibility is recreating an experiment by a different researcher. Science in general struggles with repro- ducibility. MOOC experiments often contain useful code that could be used by other researchers, but that code is oftentimes not shared with others. To improve S&R in MOOC research, this thesis first identifies the challenges MOOC sci- entists encounter when trying to share code and when making their experiments reproducible. Then, user interviews are performed to further understand MOOC research and to better understand the challenges identified earlier. A conceptual experimental workflow is designed and implemented in the form of a workbench. The workbench is then evaluated. The identified challenges based on literature with regards to S&R are: (1) Difficulty in selecting the right tools, (2) lack of a standardized workflow that en- ables reprodubility and (3) manual work required to enable reproducibility with- out a clear incentive to perform that work. From the user interviews, researchers indicate experiencing these same challenges. Based on these challenges, I pro- pose an experimental workflow that focuses on making research reproducible and on sharing code. This workflow is implemented in the form of a work- bench, where researchers can create and manage their MOOC experiments. This workbench allows for sharing code between researchers and is evaluated using a real-world data science task by three actual large-scale learning analytics re- searchers and a visiting volunteer researcher. The evaluation finds that the work- bench needs more work to be suitable for actual MOOC research use and that more researcher education is needed to improve sharing and reproducibility in MOOC research.

Engagement is critical for academic learning. It's commonly believed that motivating students to learn is crucial in education. We think that by providing students some interesting content based on what they are learning is a good idea. Since TED Talks share attractive new ideas, we are planning to motivate students by recommending TED Talks relevant to their learning content. Also, we found it's important to have some ``teasing texts'', which are used to convince students to watch TED Talks we recommended. to get these texts, we are going to adopt an argumentation mining technique called ``Claim Extraction'' on TED Talk subtitles.Claim extraction uses classifiers trained on a dataset to extract claim sentences from the given texts. And these claim sentences can be used as the ``teasing texts''. Due to the fact that there isn't any TED Talk based corpus and building one is extremely expensive, we have to train classifiers on the existing Wikipedia dataset. It means we have to deal with the cross-domain learning problem. This thesis will introduce our approach of building a TED Talk claim extraction system. This system will use classifiers trained on existing corpus and can extract claim sentences from TED Talk subtitles. Also, this thesis proposes using claims extracted from TED Talk subtitles can promote students to watch the recommended TED Talks.