Measuring learning that is hard to measure: using the PECSL model to evaluate implicit smart learning
Abstract This paper explores potential ways of evaluating the implicit learning that may be present in autonomous smart learning activities and environments, reflecting on prior phenomenographic research into smart learning activities positioned as local journeys in urban connected public spaces. Im...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2022-07-01
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| Series: | Smart Learning Environments |
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| Online Access: | https://doi.org/10.1186/s40561-022-00206-w |
| _version_ | 1811287436149391360 |
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| author | Pen Lister |
| author_facet | Pen Lister |
| author_sort | Pen Lister |
| collection | DOAJ |
| description | Abstract This paper explores potential ways of evaluating the implicit learning that may be present in autonomous smart learning activities and environments, reflecting on prior phenomenographic research into smart learning activities positioned as local journeys in urban connected public spaces. Implicit learning is considered as intrinsic motivation, value and richer engagement by participants, demonstrating levels of experience complexity, interpreted as levels of implicit learning. The paper reflects on ideas for evaluating implicit smart learning through planning for experience complexity in the context of a pedagogical model, the Pedagogy of Experience Complexity for Smart Learning (PECSL), developed from the research. By supplementing this model with further conceptual mechanisms to describe experience complexity as surface to deep learning alongside cognitive domain taxonomy equivalences, implicit smart learning might be evaluated in broad flexible ways to support the design of more effective and engaging activities. Examples are outlined placing emphasis on learner generated content, learner-directed creative learning and supporting dialogue and reflection, attempting to illustrate how implicit learning might manifest and be evaluated. |
| first_indexed | 2024-04-13T03:18:21Z |
| format | Article |
| id | doaj.art-467d56fa07d74b4d8d2b9088301c8011 |
| institution | Directory Open Access Journal |
| issn | 2196-7091 |
| language | English |
| last_indexed | 2024-04-13T03:18:21Z |
| publishDate | 2022-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Smart Learning Environments |
| spelling | doaj.art-467d56fa07d74b4d8d2b9088301c80112022-12-22T03:04:50ZengSpringerOpenSmart Learning Environments2196-70912022-07-019112110.1186/s40561-022-00206-wMeasuring learning that is hard to measure: using the PECSL model to evaluate implicit smart learningPen Lister0Faculty of Education, c/o Department of Leadership for Learning and Innovation, University of Malta, Msida CampusAbstract This paper explores potential ways of evaluating the implicit learning that may be present in autonomous smart learning activities and environments, reflecting on prior phenomenographic research into smart learning activities positioned as local journeys in urban connected public spaces. Implicit learning is considered as intrinsic motivation, value and richer engagement by participants, demonstrating levels of experience complexity, interpreted as levels of implicit learning. The paper reflects on ideas for evaluating implicit smart learning through planning for experience complexity in the context of a pedagogical model, the Pedagogy of Experience Complexity for Smart Learning (PECSL), developed from the research. By supplementing this model with further conceptual mechanisms to describe experience complexity as surface to deep learning alongside cognitive domain taxonomy equivalences, implicit smart learning might be evaluated in broad flexible ways to support the design of more effective and engaging activities. Examples are outlined placing emphasis on learner generated content, learner-directed creative learning and supporting dialogue and reflection, attempting to illustrate how implicit learning might manifest and be evaluated.https://doi.org/10.1186/s40561-022-00206-wSmart pedagogySmart learningImplicit learningLearner experienceEvaluationMotivation |
| spellingShingle | Pen Lister Measuring learning that is hard to measure: using the PECSL model to evaluate implicit smart learning Smart Learning Environments Smart pedagogy Smart learning Implicit learning Learner experience Evaluation Motivation |
| title | Measuring learning that is hard to measure: using the PECSL model to evaluate implicit smart learning |
| title_full | Measuring learning that is hard to measure: using the PECSL model to evaluate implicit smart learning |
| title_fullStr | Measuring learning that is hard to measure: using the PECSL model to evaluate implicit smart learning |
| title_full_unstemmed | Measuring learning that is hard to measure: using the PECSL model to evaluate implicit smart learning |
| title_short | Measuring learning that is hard to measure: using the PECSL model to evaluate implicit smart learning |
| title_sort | measuring learning that is hard to measure using the pecsl model to evaluate implicit smart learning |
| topic | Smart pedagogy Smart learning Implicit learning Learner experience Evaluation Motivation |
| url | https://doi.org/10.1186/s40561-022-00206-w |
| work_keys_str_mv | AT penlister measuringlearningthatishardtomeasureusingthepecslmodeltoevaluateimplicitsmartlearning |