Creativity, characterized by the capacity to generate novel and valuable ideas or solutions through imaginative thinking and unique problem-solving, differs widely between individuals.Despite its importance, this variability is often overlooked in research on personalization in education.In this study, our goal is to personalize creativity within a programming learning platform for school students.Leveraging a unique dataset of students' initial coding attempts, we employ a Gaussian Mixture Model to identify distinct creativity profiles among learners.By integrating these insights into user modeling, this work lays the foundation for developing personalized programming curricula tailored to each student's creative strengths, highlighting the potential of creativity-aware adaptive systems in education.We make our data and code publicly available at: https://github.com/sveron/Creativity.

Abstract Using examples and non-examples is a common technique to demonstrate concepts’ characteristics and boundaries. Based on their properties, certain objects are accepted as examples or non-examples intuitively, while others are accepted or neglected non-intuitively. This 2*2 classification is powerful when designing technology-enhanced learning experiences in which feedback could be provided in real-time. That is, feedback could be based not only on the correctness of student response, but also on the specifics of the objects with which they were engaged. Following this framework, we developed an interactive sorting task that aims at strengthening elementary school students’ understanding of reflective symmetry. We studied learners’ interaction with the objects presented to them, and their success. Our study included 29 elementary school students (ages 9 to 12) from both Israel and Germany. We used screen recording to code participants’ shape-movements, and defined quantitative measures of these movements. Our findings support the need for designing feedback that takes into consideration object’s properties and students’ behavior.

The current study compares textual and symbolic elaborated, response-based feedback in mathematics. We use a randomized experiment in Khan Academy to measure feedback effect in four different topics. Overall, we point out to the superiority of symbolic feedback.

Creativity has long been suggested as an important factor in learning. In this paper, we present a preliminary study of creativity in an online programming learning environment. We operationalize creativity using an existing scheme for scoring it, and then measure it automatically based on the system log files. We analyze the data in order to explore the associations between creativity and personal/contextual variables. Creativity is associated with contextual variables and is not associated with personal variables. Directions for continuing this research are discussed.

In today's job market, computer skills are part of the prerequisites for many jobs. In this paper, we report on a study of readiness to work with computers (the dependent variable) among unemployed women (N=54) after participating in a unique training focused on computer skills and empowerment. Associations were explored between this variable and 17 variables from four categories: log-based, computer literacy and experience, job-seeking motivation and practice, and training satisfaction. Only two variables were associated with the dependent variable: Knowledge post-test duration and satisfaction with content. Building a prediction model of the dependent variable, another feature was highlighted: Total number of actions in the course website along the course. Our analyses highlight the predominance of the log-based variables over the variables from the other categories, and we thoroughly discuss this finding.

Both gaming the system (taking advantage of the system's feedback and help to succeed in the tutor without learning the material) and being off-task (engaging in behavior that does not involve the system or the learning task) have been previously shown to be associated with poorer learning. In this paper we investigate two hypotheses about the mechanisms that lead to this reduced learning: (a) less learning within individual steps (immediate harmful impact) and (b) overall learning loss due to fewer opportunities to practice (aggregate harmful impact). We show that gaming tends to have immediate harmful impact while off-task tends to have aggregated harmful impact on learning.