English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The present study investigated the performance of textbased explanation for a large number of learners in an online tutoring task guided by a Pedagogical Conversational Agent (PCA). In the study, a lexical network analysis that focused on the co-occurrence of keywords in learner’s explanation text, which were used as dependent variables, was performed. This method was used to investigate how the variables, which consisted of expressions of emotion, embodied characteristics of the PCA, and personal characteristics of the learner, influenced the performance of the explanation text. The learners (participants) were students enrolled in a psychology class. The learners provided explanations to a PCA one-on-one as an after-school activity. In this activity, the PCA, portraying the role of a questioner, asked the learners to explain a key concept taught in their class. The students were randomly assigned one key term out of 30 and were asked to formulate explanations by answering different types of questions. The task consisted of 17 trials. More than 300 text-based explanation dialogues were collected from learners using a web-based explanation system, and the factors influencing learner performance were investigated. Machine learning results showed that during the explanation activity, the expressions used and the gender of the PCA influenced learner performance. Results showed that (1) learners performed better when a male PCA expressed negative emotions as opposed to when a female PCA expressed negative emotions, and (2) learners performed better when a female PCA expressed positive expressions as opposed to when a female PCA expressed negative expressions. This paper provides insight into capturing the behavior of humans performing online tasks, and it puts forward suggestions related to the design of an efficient online tutoring system using PCA. key words: online tutoring, explanation activities, pedagogical conversational agent, emotion

Lexical Network Analysis on an Online Explanation Task: Effects of Affect and Embodiment of a Pedagogical Agent