Content and quantity of highlights and annotations predict learning from multiple digital texts

Alexandra List, Chang Jen Lin

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Learning from multiple texts is an essential yet challenging academic task. Thus, a persistent need in the field is identifying those strategies that will support students' learning from multiple texts, and facilitating their use. In this study, we adopt a quasi-experimental design to examine how the use of digital annotations, allowing undergraduates (N = 278) to identify and, potentially, link important concepts across texts, can be used to foster learning from multiple texts. Additionally, we examine students' digital highlights and annotations under four different strategy conditions, assigned to elicit different forms of processing found to be effective in prior work. These conditions included asking students to highlight and annotate (a) important or relevant information in texts, (b) connections or links across texts (i.e., intertextual reasoning condition), (c) information in texts facilitating evaluation, and (d) difficult to understand information (i.e., metacognitive monitoring condition). Some differences were found in students' rendering of highlights and annotations across strategy conditions, however, no differences in multiple text comprehension and integration performance were found across strategy conditions. Still, membership in the metacognitive monitoring strategy condition, relative to the relevance condition, as well as students’ highlighting of information supporting evaluation (i.e., source information, statistical evidence), and accessing of references during reading were predictive of multiple text comprehension and integration. Thus, this study contributes to theory by providing further evidence of strategies (e.g., metacognitive monitoring, evaluation) supportive of multiple text learning and introduces a promising means whereby such strategy use may be fostered.

Original languageEnglish (US)
Article number104791
JournalComputers and Education
Volume199
DOIs
StatePublished - Jul 2023

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • Education

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