Matching Items (3)
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- All Subjects: Bayesian knowledge tracing
- Creators: Walker, Erin
- Resource Type: Text
Description
Last Hymn was created by the team of Tyler Pinho, Jefferson Le, and Curtis Spence with the desire to create an eccentric Role Playing Game focused on the exploration of a strange, dying world. Battles in the game are based off of rhythm games like Dance Dance Revolution using a procedural generation algorithm that makes every encounter unique. This is then complemented with the path system where each enemy has unique rhythm patterns to give them different types of combat opportunities. In Last Hymn, the player arrives on a train at the World's End Train Station where they are greeted by a mysterious figure and guided to the Forest where they witness the end of the world and find themselves back at the train station before they left for the Forest. With only a limited amount of time per cycle of the world, the player must constantly weigh the opportunity cost of each decision, and only with careful thought, conviction, and tenacity will the player find a conclusion from the never ending cycle of rebirth. Blending both Shinto architecture and modern elements, Last Hymn used a "fantasy-chic" aesthetic in order to provide memorable locations and dissonant imagery. As the player explores they will struggle against puzzles and dynamic, rhythm based combat while trying to unravel the mystery of the world's looping time. Last Hymn was designed to develop innovative and dynamic new solutions for combat, exploration, and mapping. From this project all three team members were able to grow their software development and game design skills, achieving goals like improved level design, improved asset pipelines while simultaneously aiming to craft an experience that will be unforgettable for players everywhere.
ContributorsPinho, Tyler (Co-author) / Le, Jefferson (Co-author) / Spence, Curtis (Co-author) / Nelson, Brian (Thesis director) / Walker, Erin (Committee member) / Kobayashi, Yoshihiro (Committee member) / Computer Science and Engineering Program (Contributor) / Computing and Informatics Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Embedded assessment constantly updates a model of the student as the student works on instructional tasks. Accurate embedded assessment allows students, instructors and instructional systems to make informed decisions without requiring the student to stop instruction and take a test. This thesis describes the development and comparison of several student models for Dragoon, an intelligent tutoring system. All the models were instances of Bayesian Knowledge Tracing, a standard method. Several methods of parameterization and calibration were explored using two recently developed toolkits, FAST and BNT-SM that replaces constant-valued parameters with logistic regressions. The evaluation was done by calculating the fit of the models to data from human subjects and by assessing the accuracy of their assessment of simulated students. The student models created using node properties as subskills were superior to coarse-grained, skill-only models. Adding this extra level of representation to emission parameters was superior to adding it to transmission parameters. Adding difficulty parameters did not improve fit, contrary to standard practice in psychometrics.
ContributorsGrover, Sachin (Author) / VanLehn, Kurt (Thesis advisor) / Walker, Erin (Committee member) / Shiao, Ihan (Committee member) / Arizona State University (Publisher)
Created2015
Description
EMBRACE (Enhanced Moved By Reading to Accelerate Comprehension in English) is an IPad application that uses the Moved By Reading strategy to help improve the reading comprehension skills of bilingual (Spanish speaking) English Language Learners (ELLs). In EMBRACE, students read the text of a story and then move images corresponding to the text that they read. According to the embodied cognition theory, this grounds reading comprehension in physical experiences and thus is more engaging.
In this thesis, I used the log data from 20 students in grades 2-5 to design a skill model for a student using EMBRACE. A skill model is the set of knowledge components that a student needs to master in order to comprehend the text in EMBRACE. A good skill model will improve understanding of the mistakes students make and thus aid in the design of useful feedback for the student.. In this context, the skill model consists of vocabulary and syntax associated with the steps that students performed. I mapped each step in EMBRACE to one or more skills (vocabulary and syntax) from the model. After every step, the skill level is updated in the model. Thus, if a student answered the previous step incorrectly, the corresponding skills are decremented and if the student answered the previous question correctly, the corresponding skills are incremented, through the Bayesian Knowledge Tracing algorithm.
I then correlated the students’ predicted scores (computed from their skill levels) to their posttest scores. I evaluated the students’ predicted scores (computed from their skill levels) by comparing them to their posttest scores. The two sets of scores were not highly correlated, but the results gave insights into potential improvements that could be made to the system with respect to user interaction, posttest scores and modeling algorithm.
In this thesis, I used the log data from 20 students in grades 2-5 to design a skill model for a student using EMBRACE. A skill model is the set of knowledge components that a student needs to master in order to comprehend the text in EMBRACE. A good skill model will improve understanding of the mistakes students make and thus aid in the design of useful feedback for the student.. In this context, the skill model consists of vocabulary and syntax associated with the steps that students performed. I mapped each step in EMBRACE to one or more skills (vocabulary and syntax) from the model. After every step, the skill level is updated in the model. Thus, if a student answered the previous step incorrectly, the corresponding skills are decremented and if the student answered the previous question correctly, the corresponding skills are incremented, through the Bayesian Knowledge Tracing algorithm.
I then correlated the students’ predicted scores (computed from their skill levels) to their posttest scores. I evaluated the students’ predicted scores (computed from their skill levels) by comparing them to their posttest scores. The two sets of scores were not highly correlated, but the results gave insights into potential improvements that could be made to the system with respect to user interaction, posttest scores and modeling algorithm.
ContributorsFurtado, Nicolette Dolores (Author) / Walker, Erin (Thesis advisor) / Hsiao, Ihan (Committee member) / Restrepo, M. Adelaida (Committee member) / Arizona State University (Publisher)
Created2016