Monitoring Conceptual Development with Meaningful Interaction Analysis

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Invited presentation given at the 2nd workshop on Natural Language Processing in Support of Learning: Metrics, Feedback and Connectivity (NLPSL'10) in Bucharest, Romania, September 15, 2010.

Technology

Monitoring Conceptual Development Fridolin WildKMi, The Open University

Tying shoelaces Douglas Adams’ ‘meaning of liff’: Epping: The futile movements of forefingers and eyebrows used when failing to attract the attention of waiters and barmen. Shoeburyness: The vague uncomfortable feeling you get when sitting on a seat which is still warm from somebody else's bottom I have been convincingly Sapir-Whorfed by this book. Concepts things we can (easily) learn from or express in language

Meaningful Interaction Analysis Two-Mode factor analysis of the co-occurrences in the terminology Results in a latent-semantic vector space Which can be analysed with Network Analysis

disambiguation with context heterogeneous corpus The mathemagics behind Meaningful Interaction Analysis

associative closeness meaning space The mathemagics behind Meaningful Interaction Analysis

network analysis is used to identify communities of related understanding The mathemagics behind Meaningful Interaction Analysis

Usage Example: Reflecting on Conceptual Change Reflection is an interactive process of creative sense-making of the past

Internal latent-semantic graph structure (MIA output)

Software Support: Conceptual Inspection Analytics

Evaluation Evaluating effectiveness: measure of the accuracy in representing conceptual development Can be measured with two complementary methods by assessing the external validity of: Concept Annotation: effectiveness in selecting accurate conceptual descriptors (with ratings) Concept Proximity: effectiveness in representing proximity (with card-sorts) By comparing against human ratings of 18 first-year medical students of the University of Manchester Medical School aged 19-21

Concept Annotation Annotation of 5 authentic postings again on ‘safe prescribing’ Selection of 10 top-loading concepts Adding of 5 random distracters Participants ranked on Likert Scale of 1 to 5how good the concept described the posting Human Interrater Correlation was measured with free marginal Kappa (Randolph, 2005) Conflated categories (1+2,3,4+5)

Concept Proximity (1) Four authentic learner blog postings about ‘safe prescribing’ generated ~ 50 top-loading concepts each Printed on cards Participants grouped them in piles Comparison of participant clustering with kmeans-based clustering in the MIA space 1% of term pairs put into same cluster by more than 12 participants 7% by between 7 and 12 1% term pairs: Spearman’s Rho as interrater correlation

Proximity (3) Silhouette width in the MIA space (Rousseeuw, 1986) Silhouette plots depict for each observation, how good the balance between its distances to its other cluster members compared to its distances within the next close cluster is.

Conclusion 1st year students do not have much agreement in rating the annotations, could be a sign of heterogeneous frames of reference Activation strength of the 10 concepts has not been taken into account (would be interesting!) Still: pretty good clustering results in the upper range Lower range: could be an artefact of the clustering (clustering of a folded-in posting, not clustering in the space) All in all: points towards rigorous use of thresholds Near human results (at the human overlap) Near human results (producing clearly better results than chance, but no perfect agreement)

Latent Semantic Analysis (LSA) is a mathematical technique for computationally modeling the meaning of words and larger units of texts. LSA works by applying a mathematical technique called Singular Value Decomposition (SVD) to a term*document matrix containing frequency counts for all words found in the corpus in all of the documents or passages in the corpus. After this SVD application, the meaning of a word is represented as a vector in a multidimensional semantic space, which makes it possible to compare word meanings, for instance by computing the cosine between two word vectors. LSA has been successfully used in a large variety of language related applications from automatic grading of student essays to predicting click trails in website navigation. In Coh-Metrix (Graesser et al. 2004), a computational tool that produces indices of the linguistic and discourse representations of a text, LSA was used as a measure of text cohesion by assuming that cohesion increases as a function of higher cosine scores between adjacent sentences. Besides being interesting as a technique for building programs that need to deal with semantics, LSA is also interesting as a model of human cognition. LSA can match human performance on word association tasks and vocabulary test. In this talk, Fridolin will focus on LSA as a tool in modeling language acquisition. After framing the area of the talk with sketching the key concepts learning, information, and competence acquisition, and after outlining presuppositions, an introduction into meaningful interaction analysis (MIA) is given. MIA is a means to inspect learning with the support of language analysis that is geometrical in nature. MIA is a fusion of latent semantic analysis (LSA) combined with network analysis (NA/SNA). LSA, NA/SNA, and MIA are illustrated by several examples.

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Monitoring Conceptual Development with Meaningful Interaction Analysis

1. Monitoring Conceptual Development Fridolin WildKMi, The Open University

2. Tying shoelaces Douglas Adams’ ‘meaning of liff’: Epping: The futile movements of forefingers and eyebrows used when failing to attract the attention of waiters and barmen. Shoeburyness: The vague uncomfortable feeling you get when sitting on a seat which is still warm from somebody else's bottom I have been convincingly Sapir-Whorfed by this book. Concepts things we can (easily) learn from or express in language

3. Semantic = Meaning = …

4. Meaningful Interaction Analysis Two-Mode factor analysis of the co-occurrences in the terminology Results in a latent-semantic vector space Which can be analysed with Network Analysis

5. disambiguation with context heterogeneous corpus The mathemagics behind Meaningful Interaction Analysis

6. associative closeness meaning space The mathemagics behind Meaningful Interaction Analysis

7. network analysis is used to identify communities of related understanding The mathemagics behind Meaningful Interaction Analysis

8. Usage Example: Reflecting on Conceptual Change Reflection is an interactive process of creative sense-making of the past

9. Capturing traces in text

10. Internal latent-semantic graph structure (MIA output)

11. Software Support: Conceptual Inspection Analytics

12. Emergent Reference Models

13. Evaluation Evaluating effectiveness: measure of the accuracy in representing conceptual development Can be measured with two complementary methods by assessing the external validity of: Concept Annotation: effectiveness in selecting accurate conceptual descriptors (with ratings) Concept Proximity: effectiveness in representing proximity (with card-sorts) By comparing against human ratings of 18 first-year medical students of the University of Manchester Medical School aged 19-21

14. Concept Annotation Annotation of 5 authentic postings again on ‘safe prescribing’ Selection of 10 top-loading concepts Adding of 5 random distracters Participants ranked on Likert Scale of 1 to 5how good the concept described the posting Human Interrater Correlation was measured with free marginal Kappa (Randolph, 2005) Conflated categories (1+2,3,4+5)

15. Concept Proximity (1) Four authentic learner blog postings about ‘safe prescribing’ generated ~ 50 top-loading concepts each Printed on cards Participants grouped them in piles Comparison of participant clustering with kmeans-based clustering in the MIA space 1% of term pairs put into same cluster by more than 12 participants 7% by between 7 and 12 1% term pairs: Spearman’s Rho as interrater correlation

16. Proximity (2)

17. Proximity (3) Silhouette width in the MIA space (Rousseeuw, 1986) Silhouette plots depict for each observation, how good the balance between its distances to its other cluster members compared to its distances within the next close cluster is.

18. Conclusion 1st year students do not have much agreement in rating the annotations, could be a sign of heterogeneous frames of reference Activation strength of the 10 concepts has not been taken into account (would be interesting!) Still: pretty good clustering results in the upper range Lower range: could be an artefact of the clustering (clustering of a folded-in posting, not clustering in the space) All in all: points towards rigorous use of thresholds Near human results (at the human overlap) Near human results (producing clearly better results than chance, but no perfect agreement)

19. The End

Monitoring Conceptual Development with Meaningful Interaction Analysis

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