| Description |
This paper examines the effectiveness of content-driven prompts optimized over time in facilitating preservice teachers' self-regulated learning with network-based tutors. The optimization problem consists of finding the best content from all feasible content alluded to in the learning environment to facilitate information processing. Preservice teachers (N = 40) were randomly assigned to either a prompt or no-prompt condition and revised the design of a lesson to integrate technology. Several metrics were extracted from log trace data to characterize the following: (1) the breakdown of content-driven prompt convergence and lexical overlap with learner revisions; (2) the operations that mediate information seeking (i.e., elapsed time, amount of characters, and topic mixtures); and (3) their outcomes (i.e., quality score for the desirability of linguistic content). Results showed the content of prompts converged towards information assimilated by learners in accordance with the network topology, but that this process was unstable. This finding may be attributed to how learner revisions varied form original text, as the exam of cross-distances in the similarity of the original and revised information from the lesson suggests that the majority of learners chose to alter, rather than quote or paraphrase information. A linear mixed-effects model was used to predict the quality of learner revisions on the basis of various facets of information seeking behaviors. Learner revisions related to content knowledge were associated with greater quality scores when learners requested a prompt, but not when learners failed to make a iv request. We discuss the possibility that the content of prompts should be optimized over time at first on the basis of information highlighted by learners from an online lesson, then from the quality of the revision saved by a learner and detected by the system. |
