{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T18:13:27Z","timestamp":1789150407518,"version":"build-2803163510"},"reference-count":51,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2017,8,12]],"date-time":"2017-08-12T00:00:00Z","timestamp":1502496000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Brit J of Edu Psychol"],"published-print":{"date-parts":[[2018,3]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>\n                      Self\u2010regulated learning (\n                      <jats:styled-content style=\"fixed-case\">SRL<\/jats:styled-content>\n                      ) models position metacognitive monitoring as central to\n                      <jats:styled-content style=\"fixed-case\">SRL<\/jats:styled-content>\n                      processing and predictive of student learning outcomes (Winne &amp; Hadwin, 2008; Zimmerman, 2000). A body of research evidence also indicates that depth of strategy use, ranging from surface to deep processing, is predictive of learning performance.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>\n                      In this study, we investigated the relationships among the frequency of metacognitive monitoring and the utilization of deep and surface\u2010level strategies, and the connections between these\n                      <jats:styled-content style=\"fixed-case\">SRL<\/jats:styled-content>\n                      processes and learning outcomes across two academic domains, science and history.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Sample<\/jats:title>\n                    <jats:p>\n                      This was a secondary data analysis of two studies. The first study sample was 170 undergraduate students from a University in the south\u2010eastern United States. The second study sample consisted of 40\n                      <jats:styled-content style=\"fixed-case\">US<\/jats:styled-content>\n                      high school students in the same area.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We collected think\u2010aloud protocol\n                      <jats:styled-content style=\"fixed-case\">SRL<\/jats:styled-content>\n                      and knowledge measure data and conducted both structural equation\u00a0modelling and path analysis to investigate our research questions.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Findings showed across both studies and two distinct academic domains, students who enacted more frequent monitoring also enacted more frequent deep strategies resulting in better performance on academic evaluations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>These findings suggest the importance of measuring not only what depth of strategies learners use, but also the degree to which they monitor their learning. Attention to both is needed in research and practice.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/bjep.12174","type":"journal-article","created":{"date-parts":[[2017,8,12]],"date-time":"2017-08-12T03:26:39Z","timestamp":1502508399000},"page":"63-79","source":"Crossref","is-referenced-by-count":44,"title":["Monitoring and depth of strategy use in computer\u2010based learning environments for science and history"],"prefix":"10.1111","volume":"88","author":[{"given":"Victor M.","family":"Deekens","sequence":"first","affiliation":[{"name":"University of North Carolina at Chapel Hill North Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeffrey A.","family":"Greene","sequence":"additional","affiliation":[{"name":"University of North Carolina at Chapel Hill North Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikki G.","family":"Lobczowski","sequence":"additional","affiliation":[{"name":"University of North Carolina at Chapel Hill North Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2017,8,12]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.3102\/0034654316689306"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1207\/s15326985ep3004_3"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.learninstruc.2012.10.003"},{"key":"e_1_2_10_5_1","first-page":"393","volume-title":"Handbook of self\u2010regulation of learning and performance","author":"Alexander P. 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