A Model-Based Method for Information Alignment: A Case Study on Educational Standards

Abstract

We propose a model-based method for information alignment using educational standards as a case study. Discrepancies and inconsistencies in educational standards across different states/cities hinder the retrieval and sharing of educational resources. Unlike existing educational standards alignment systems that only give binary judgments (either "aligned" or "not-aligned"), our proposed system classifies each pair of educational standard statements in one of seven levels of alignments: Strongly Fully-aligned, Weakly Fully-aligned, Partially-aligned***, Partially-aligned**, Partially-aligned*, Poorly-aligned, and Not-aligned. Such a 7-level categorization extends the notion of binary alignment and provides a finer-grained system for comparing educational standards that can broaden categories of resource discovery and retrieval. This study continues our previous use of mathematics education as a domain, because of its generally unambiguous concepts. We adopt a materialization pattern (MP) model developed in our earlier work to represent each standard statement as a verb-phrase graph and a noun-phrase graph; we align a pair of statements using graph matching based on Bloom's Taxonomy, WordNet, and taxonomy of mathematics concepts. Our experiments on data sets of mathematics educational standards show that our proposed system can provide alignment results with a high degree of agreement with domain expert's judgments. Category: Smart and intelligent computing

Learning Skill Equivalencies Across Platform Taxonomies

“…The term "crosswalk" is one, derived from the idea of creating a path to cross a street, used to describe the connection between two taxonomies or sets of educational standards [7,32]. Other terms like "transfer" [29] and "alignment" [6,35] have also been used in related work. In the context of digital learning environments, several terms have been used to refer to the elements of their taxonomy.…”

“…Taxonomy mapping has relied on manual work by subject matter experts [7,29,32], though there has been past research on automating the process using legacy Natural Language Processing (NLP) to find similar skills across taxonomies using text descriptions of each skill [6,35]. Choi et al [6] converted each skill statement to a verb phrase graph and a noun phrase graph, then calculated similarity between skills by comparing graphs. Yilmazel et al [35] used rule-based methods to extract features from skill descriptions in one standard and trained a machine learning classifier to map them to another standard.…”