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Weight Criteria

AutoTutor Lite is capable of assessing student responses in real time by using a lightweight language analyzer based on a Domain Specific Semantic Processing Portal. Generally speaking, the natural language processing component of AutoTutor Lite is based on semantic space theory, specifically Latent Semantic Analysis (LSA).

If you are unfamiliar with LSA, consider the following quote from J.R Firth (1957) "You shall know a word by the company it keeps." LSA is ran on a large amount of text documents (corpus), where each term in the corpus is represented numerically. The "distance" between two terms in the semantic space can be used to judge the accuracy of student/user input during assessment in AutoTutor Lite. The "semantic answer" provided by the author is what the student/user responses are compared against. 

A central goal for AutoTutor Lite authors should be to prompt their students/users to provide more elaborative answers. Self-explanations and deep-level reasoning questions have been shown to promote learning (Chi, De Leeuw, Chiu, & LaVancher, 1994; Craig, Sullins, Witherspoon, & Gholson, 2006).  Equipped with this knowledge, AutoTutor Lite authors should carefully configure the semantic engine to best provide feedback to students/users that prompts them to produce more elaborate self-explanations. 

Semantic Space Selection

First, an AutoTutor Lite author should select an appropriate corpus for their Semantic Space. The "Free Association (by human) corpus is based on the free association norms database developed at the University of South Florida (Nelson, McEvoy, & Schreiber, 2004). The TASA corpora were developed by the Touchstone Applied Science Associates and are broken down at the grade level. These corpora consist of textbooks commonly used for specific grade levels. Both the TASA corpora and the Free Association (by human) corpus are useful for more general domains. Using these corpora provides a good starting point for AutoTutor Lite authors.  

Domain Selection
A unique feature of AutoTutor Lite is the ability to select a specific domain for the semantic space. These domains are generated by a Domain Specific Semantic Processing Portal, which essentially places a weight bias for specific terms found more frequently within the selected domain (e.g, health, science & mathematics). 

Weight Criteria
This is the minimum weight an associated term in the "Semantic answer" or "expectation" must have to be considered or included in the space. 

Minimum Rank (weight*Strength)
The minimum rank is the product of the weight criterion and strength criterion, and is essentially the value used to determine how many terms in the semantic space should be considered. The higher the value for minimum rank, the smaller percentage of the semantic space is used. 

Minimum Association Strength
The minimum association strength determines the smallest association to be considered between the "semantic answer" or expectation and associated words (i.e., nearest neighbors, synonyms, or related words). A high value here will only consider the most closely related terms to the target answer. 

Minimum Item Weight
The minimum item weight value is used as the cutoff point for the weights of student/user input. This can be used to eliminate the consideration of high frequency terms like "I" and "and". 

For a case study that explores the usage of these semantic space configurations please see "The development and analysis of tutorial dialogues in AutoTutor Lite" (Wolfe, Widmer, Reyna, Hu, Cedillos, Fisher, Brust-Renck, Williams, Vannucchi, & Weil, 2013). 

Chi, M. T., De Leeuw, N., Chiu, M. H., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive science, 18(3), 439-477.

Craig, S. D., Sullins, J., Witherspoon, A., & Gholson, B. (2006). The deep-level-reasoning-question effect: The role of dialogue and deep-level-reasoning questions during vicarious learning. Cognition and Instruction, 24(4), 565-591.

Nelson, D. L., McEvoy, C. L., & Schreiber, T. A. (2004). The University of South Florida free association, rhyme, and word fragment norms. Behavior Research Methods, Instruments, & Computers, 36, 402-407.

Wolfe, C.R., Widmer, C.L., Reyna, V.F., Hu, X., Cedillos, E. M., Fisher, C.R., Brust-Renck, P.G., Williams, T.C., Vannucchi, I.D., Weil, A.M. (2013). The development and analysis of tutorial dialogues in AutoTutor Lite. Behavior research methods, 1-14.