This article investigates the influence of time on features for the prediction of user dropout in an online training platform. Specifically, we target a comparison between the two time measurements: activity-based versus duration-based. Considering time, we utilize features in different groups: either activity-based features for activities or duration-based features for duration, as well as the start-based, action-type-based, and course-based features for both time measurements. The most surprising aspect of the results is the high accuracy of the classifiers from the tenth activity (which corresponds to almost half a day on average) onward. While the action-type-based and the course-based features have a major influence on dropout, the start-based features are only influential in the classifiers that use information of activities at beginning. In addition, the activity-based features have only a minor impact in the middle of the course whilst the duration-based features have a major influence throughout the course.

Identifying the high level structure of texts provides important information when performing distant reading analysis. The structure of texts is not necessarily linear, as transitions, such as changes in the scenery or flashbacks, can be present. As a first step in identifying this structure, we aim to identify transitions in texts. Previous work (Heyns and van Zaanen, 2021) proposed a system that can successfully identify one transition in literary texts. The text is split in snippets and LDA is applied, resulting in a sequence of topics. A transition is introduced at the point that separates the topics (before and after the point) best. In this article, we extend the existing system such that it can detect multiple transitions. Additionally, we introduce a new system that inherently handles multiple transitions in texts. The new system also relies on LDA information, but is more robust than the previous system. We apply these systems to texts with known transitions (as they are constructed by concatenating text snippets stemming from different source texts) and evaluation both systems on texts with one transition and texts with two transitions. As both systems rely on LDA to identify transitions between snippets, we also show the impact of varying the number of LDA topics on the results as well. The new system consistently outperforms the previous system, not only on texts with multiple transitions, but also on single boundary texts.

Abstract Virtual robots, including virtual animals, are expected to play a major role within affective and aesthetic interfaces, serious games, video instruction, and the personalization of educational instruction. Their actual impact, however, will very much depend on user perception of virtual characters as the uncanny valley hypothesis has shown that the design of virtual characters determines user experiences. In this article, we investigated whether the uncanny valley effect, which has already been found for the human‐like appearance of virtual characters, can also be found for animal‐like appearances. We conducted an online study (N = 163) in which six different animal designs were evaluated in terms of the following properties: familiarity, commonality, naturalness, attractiveness, interestingness, and animateness. The study participants differed in age (under 10–60 years) and origin (Europe, Asia, North America, and South America). For the evaluation of the results, we ranked the animal‐likeness of the character using both expert opinion and participant judgments. Next to that, we investigated the effect of movement and morbidity. The results confirm the existence of the uncanny valley effect for virtual animals, especially with respect to familiarity and commonality, for both still and moving images. The effect was particularly pronounced for morbid images. For naturalness and attractiveness, the effect was only present in the expert‐based ranking, but not in the participant‐based ranking. No uncanny valley effect was detected for interestingness and animateness. This investigation revealed that the appearance of virtual animals directly affects user perception and thus, presumably, impacts user experience when used in applied settings.

In recent years, a wide variety of mentorship programmes targeting issues that cannot be addressed through traditional teaching and learning methods alone have been developed. Mentoring plays significant roles in the growth and development of both mentors and mentees, and the positive impacts of mentoring have been well documented. Mentorship programmes are therefore increasingly being implemented in a wide variety of fields by organisations, academic institutes, businesses, and governments. While there is a growing body of literature on mentoring and mentorship programmes, gaining a clear overview of the field is often challenging. In this article, we therefore provide a concise summary of recommendations to consider when designing and establishing mentorship programmes. These recommendations are based on the collective knowledge and experiences of 4 different emerging and established mentorship programmes and can be adapted across various mentorship settings or contexts.

Revision plays a major role in writing and the analysis of writing processes. Revisions can be analyzed using a product-oriented approach (focusing on a finished product, the text that has been produced) or a process-oriented approach (focusing on the process that the writer followed to generate this product). Although several language resources exist for the product-oriented approach to revisions, there are hardly any resources available yet for an in-depth analysis of the process of revisions. Therefore, we provide an extensive dataset on revisions made during writing (accessible via https://hdl.handle.net/10411/VBDYGX). This dataset is based on keystroke data and eye tracking data of 65 students from a variety of backgrounds (undergraduate and graduate English as a first language and English as a second language students) and a variety of tasks (argumentative text and academic abstract). In total, 7,120 revisions were identified in the dataset. For each revision, 18 features have been manually annotated and 31 features have been automatically extracted. As a case study, we show two potential use cases of the dataset. In addition, future uses of the dataset are described.

Languages vary in the way stress is assigned to syllables within words. This article investigates the learnability of stress systems in a wide range of languages. The stress systems can be described using finite-state automata with symbols indicating levels of stress (primary, secondary, or no stress). Finite-state automata have been the focus of research in the area of grammatical inference for some time now. It has been shown that finite-state machines are learnable from examples using state-merging. One such approach, which aims to learn k-testable languages, has been applied to stress systems with some success. The family of k-testable languages has been shown to be efficiently learnable (in polynomial time). Here, we extend this approach to k, l-local languages by taking not only left context, but also right context, into account. We consider empirical results testing the performance of our learner using various amounts of context (corresponding to varying definitions of phonological locality). Our results show that our approach of learning stress patterns using state-merging is more reliant on left context than on right context. Additionally, some stress systems fail to be learned by our learner using either the left-context k-testable or the left-and-right-context k, l-local learning system. A more complex merging strategy, and hence grammar representation, is required for these stress systems.