Concept Science Evidence-Based MERLO Learning Analytics

Concept Science Evidence-Based MERLO Learning Analytics

Uri Shafrir (University of Toronto, Canada) and Ron S. Kenett (KPA Group Ltd., Israel & University of Turin, Italy)
DOI: 10.4018/978-1-4666-9634-1.ch016
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Abstract

This chapter is about Concept Science Evidence-Based MERLO Learning Analytics, an educational informatics system based on the teaching and learning methodologies described in the chapter on “Learning in the Digital Age with Meaning Equivalence Reusable Learning Objects (MERLO)” (Etkind, Kenett, & Shafrir, 2015). It collects, documents, analyzes, and reports data gathered from implementation of a pedagogy for conceptual thinking and peer cooperation in elementary, secondary, and post-secondary educational institutions, as well as from learning programs in private and public organizations.
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Introduction

Concept Science Evidence-Based MERLO Learning Analytics is an educational informatics program that collects, analyzes, and reports data generated from implementation of a pedagogy for conceptual thinking and peer cooperation (Etkind et al, 2015). This pedagogy brings into focus the learners’ attention on meaning by using learning activities with different representations of conceptual content that may – or may not – share equivalence-of-meaning.

The chapter covers the following topics: The first section titled BACKGROUND presents recent developments in learning analytics, showing how Concept Science Evidence-Based MERLO Learning Analytics is different from earlier learning analytics informatics that mostly focuses on technological issues, such as digital content delivery and interactive learning support. The next section (THE MAIN FOCUS OF THE CHAPTER) discusses three interactive streams of learning activities in this novel pedagogy: (1) Interactive Concept Discovery (InCoD) with semiotic searches of a Knowledge Repository (KR) of digital documents relevant to the course content (Shafrir & Etkind, 2011). (2) Formative and summative assessments with Meaning Equivalence Reusable Learning Objects (MERLO) (Arzarello, Kenett, Robutti, Shafrir, Prodromou, & Carante, 2015; Etkind, Kenett, & Shafrir, 2010; Etkind & Shafrir, 2011; Shafrir & Etkind, 2006). (3) Development of learners’ meta-cognitive and higher order thinking skills: cooperation; knowledge of being observed; reflection and self-evaluation (Nowak et al., 2011; Donovan & Bransford, 2005; Bloom, Krathwohl, & Masia, 1956; Anderson, Krathwohl, & Bloom, 2001). The third section (SOLUTIONS AND RECOMMENDATIONS) describes learning outcomes of pedagogy for conceptual thinking and peer cooperation. These are documented and presented in Concept Science Evidence-Based MERLO Learning Analytics at the learner and instructor levels, as well as at the level of educational institutions’ policy making.

Key Terms in this Chapter

Meaning Equivalence Reusable Learning Objects (MERLO): A multi-dimensional database used for enhancing conceptual learning and performing formative and summative assessments. The database is created by sorting and mapping important concepts in a domain of knowledge through multi-semiotic representations in multiple sign systems.

Diagnostics of Misconceptions: A type of depressed score on MERLO indicates that the learner fails to include within the Boundary of Meaning (BoM) of the concept certain statements that do share equivalence-of-meaning (but do not share surface similarity) with the Target Statement; such depressed score signals an over-restrictive (too exclusive) understanding of the meaning of the concept . Another type of depressed score on MERLO indicates that the learner fails to exclude from the Boundary of Meaning (BoM) of the concept certain statements that do not share equivalence of-meaning (but that do share surface similarity) with the Target Statement; this latter type of depressed score signals an under-restrictive (too inclusive) understanding of the meaning of the concept .

Concept Science: A novel generic methodology for parsing and analyzing concepts, applicable to the various knowledge domains and professions. Concept science use tools for recognizing, representing, organizing, exploring, communicating, and manipulating knowledge encoded in controlled vocabularies of sublanguages.

Interactive Concept Discovery (InCoD): A novel semantic search learning tool with an intuitive, interactive procedure that allows a learner to search large digital databases (eJournals; eBooks; databases; eArchives), and to discover the building blocks of a lexical label of a concept within a particular context of the knowledge domain, namely, co-occurring subordinate concepts and relations.

Learning Analytics: A procedure that use big data for measurement, analysis and reporting of information on individual learners for optimizing learning.

Boundary of Meaning (BoM): Given a community of specialists that share a sublanguage, and a Target Statement that encodes a particular conceptual situation, then BoM is defined as the boundary between two mutually exclusive semantic spaces in the sublanguage: a semantic space that contains only representations that do share equivalence-of-meaning with the Target Statement; and a semantic space that contains only representations that do not share equivalence-of-meaning with the Target Statement.

Formative Assessment: A weekly quiz that provide learners with opportunities to cooperate through discussions of a MERLO item in small groups; then make their own decisions and send their individual responses to the instructor’s computer via mobile communication devices; followed by class discussion.

Concept: Labeled pattern in human experience.

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