The present study investigated the performance of text-based 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.