Contextualization in education connects learning material to students' experiences and background knowledge to make it relevant and meaningful. It involves placing content in a real-world context to show students the practical applications and relevance of the concepts they are learning. Contextualizing is an important pedagogical strategy. In order to support teachers in this effort, it is necessary to understand what contexts to provide for teachers to make relevant instruction in certain situations. In this paper, we aim to explore: In what context do teachers need students' everyday life experiences to make the class more relevant to students? We conducted focus groups with teachers who shared their contextualizing experiences. The context for contextualizing refers to the information used to characterize the situation in the classroom and the experiences of the students. The results of this research include the three categories of context information, time, activity, and location, that are relevant to the lesson and the students' prior experiences related to these factors. Design implications are discussed in this paper to illustrate how adaptive teacher support systems can provide relevant and meaningful information to the teachers by considering context information and students' prior experiences.

During instruction, using information that is directly relevant to students within a lesson creates a more personalized lesson that may better resonate with the students. However, in a class, each student has highly varied individual experiences and it is untenable for the teacher to remember all of the experiences. Furthermore, teaching already involves substantial multitasking that may be quite cognitively overwhelming, even with non-personalized lessons. Therefore, providing support to the teacher to incorporate students’ individual experiences is crucial to enable truly personalized teaching. We propose that visualizing students’ individual experiences for the teacher during instruction may help him or her adapt the lesson in real-time to the students in the class. This paper investigates the kind of visualization that would enable a teacher to uptake a student’s experiences the most effectively. We conducted a within-subjects study to assess the impact that three forms of visualization (plain text, graphical mind map, and story illustration) would have on visual efficiency during a hypothetical teaching scenario. Visual efficiency was measured in terms of response accuracy, response time, and mental effort. Results suggested that visual efficiency can be improved by using proper visualization design. We discuss the implications for supporting real-time personalized instruction.

Storytelling is a critical step in the cognitive development of children. Particularly, this requires children to mentally project into the story context and to identify with the thoughts of the characters in their stories. We propose to support free imagination in creative storytelling through an enactment-based approach that allows children to embody an avatar and perform as the story character. We designed our story creation interface with two modes of avatar: the story-relevant avatar and the self-avatar, to investigate the effects of avatar design on the quality of children's creative products. In our study with 20 child participants, the results indicate that self-avatars can create a stronger sense of identification and embodied presence, while story-relevant avatars can provide a scaffold for mental projection.

It is becoming increasingly common for intelligent systems, such as recommender systems, to provide explanations for their generated recommendations to the users. However, we still do not have a good understanding of what types of explanations work and what factors affect the effectiveness of different types of explanations. Our work focuses on explanations for movie recommender systems. This paper presents a mixed study where we hypothesize that the type of explanation, as well as user motivation for watching movies, will affect how users respond to recommendation system explanations. Our study compares three types of explanations: i) neighbor-ratings, ii) profile-based, and iii) event-based, as well as three types of user movie-watching motivations: i) hedonic (fun and relaxation), ii) eudaimonic (inspiration and meaningfulness), and iii) educational (learning new content). We discuss the implications of the study results for the design of explanations for movie recommender systems, and future novel research directions that the study results uncover.