In recent years, the flipped classroom has been proposed as an alternative paradigm of teaching, and has been demonstrated to improve the students' knowledge and skills, engagement, and self-efficacy. At the same time, as the number of students seeking higher education is growing and the needs of these students are rapidly evolving, it has become necessary to improve their cognitive holding power and enhance equity among them. The goal of this study is to investigate the impact of the flipped classroom on improving the students' cognitive holding power and enhancing equity.

The cold-start scenario is a critical problem for recommendation systems, especially in dynamically changing domains such as online news services. In this research, we aim at addressing the cold-start situation by adapting an unsupervised neural User2Vec method to represent new users and articles in a multidimensional space. Toward this goal, we propose an extension of the Doc2Vec model that is capable of representing users with unknown history by building embeddings of their metadata labels along with item representations. We evaluate our proposed approach with respect to different parameter configurations on three real-world recommendation datasets with different characteristics. Our results show that this approach may be applied as an efficient alternative to the factorization machine-based method when the user and item metadata are used and hence can be applied in the cold-start scenario for both new users and new items. Additionally, as our solution represents the user and item labels in the same vector space, we can analyze the spatial relations among these labels to reveal latent interest features of the audience groups as well as possible data biases and disparities.

In recent years, there have been many approaches to using robots to teach computer programming. In intelligent tutoring systems and computer-aided learning, there is also some research to show that affective feedback to the student increases learning efficiency. However, a few studies on the role of incorporating an emotional personality in the robot in robot-assisted learning have found different results. To explore this issue further, we conducted a pilot study to investigate the effect of positive verbal encouragement and non-verbal emotive behaviour of the Miro-E robot during a robot-assisted programming session. The participants were tasked to program the robot’s behaviour. In the experimental group, the robot monitored the participants’ emotional state via their facial expressions, and provided affective feedback to the participants after completing each task. In the control group, the robot responded in a neutral way. The participants filled out a questionnaire before and after the programming session. The results show a positive reaction of the participants to the robot and the exercise. Though the number of participants was small, as the experiment was conducted during the pandemic, a qualitative analysis of the data was carried out. We found that the greatest affective outcome of the session was for students who had little experience or interest in programming before. We also found that the affective expressions of the robot had a negative impact on its likeability, revealing vestiges of the uncanny valley effect.

Studying interactions of children with humanoid robots in familiar spaces in natural contexts has become a key issue for social robotics. To fill this need, we conducted several Child–Robot Interaction (CRI) events with the Pepper robot in Polish and Japanese kindergartens. In this paper, we explore the role of trust and expectations towards the robot in determining the success of CRI. We present several observations from the video recordings of our CRI events and the transcripts of free-format question-answering sessions with the robot using the Wizard-of-Oz (WOZ) methodology. From these observations, we identify children’s behaviors that indicate trust (or lack thereof) towards the robot, e.g., challenging behavior of a robot or physical interactions with it. We also gather insights into children’s expectations, e.g., verifying expectations as a causal process and an agency or expectations concerning the robot’s relationships, preferences and physical and behavioral capabilities. Based on our experiences, we suggest some guidelines for designing more effective CRI scenarios. Finally, we argue for the effectiveness of in-the-wild methodologies for planning and executing qualitative CRI studies.

We conducted a comparative study to evaluate the efficacy of synchronous and asynchronous interaction modes when providing feedback for improving academic writing, achievement motivation and critical thinking. The participants undertook an academic writing task, and were given feedback by a mentor using one of three interaction modes: asynchronous, synchronous e-feedback, and face to face. Data was collected from the participants via an assessment rubric applied to their writing after each stage of interaction, along with pretest and posttest questionnaires, and a semi-structured interview at the end. We found that synchronous feedback is more effective in increasing the quality of academic writing and achievement motivation, but for critical thinking we did not find any significant difference. We suggest that a strategy combining the advantages of each mode of interaction, considering the writers’ experience, may be the most effective way to promote academic writing, achievement motivation and critical thinking.