Matching Items (18)
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- All Subjects: Educational technology
- All Subjects: Categorization
- Creators: VanLehn, Kurt
- Creators: Glenberg, Arthur
- Resource Type: Text
Description
Most people are experts in some area of information; however, they may not be knowledgeable about other closely related areas. How knowledge is generalized to hierarchically related categories was explored. Past work has found little to no generalization to categories closely related to learned categories. These results do not fit well with other work focusing on attention during and after category learning. The current work attempted to merge these two areas of by creating a category structure with the best chance to detect generalization. Participants learned order level bird categories and family level wading bird categories. Then participants completed multiple measures to test generalization to old wading bird categories, new wading bird categories, owl and raptor categories, and lizard categories. As expected, the generalization measures converged on a single overall pattern of generalization. No generalization was found, except for already learned categories. This pattern fits well with past work on generalization within a hierarchy, but do not fit well with theories of dimensional attention. Reasons why these findings do not match are discussed, as well as directions for future research.
ContributorsLancaster, Matthew E (Author) / Homa, Donald (Thesis advisor) / Glenberg, Arthur (Committee member) / Chi, Michelene (Committee member) / Brewer, Gene (Committee member) / Arizona State University (Publisher)
Created2013
Description
This thesis is an initial test of the hypothesis that superficial measures suffice for measuring collaboration among pairs of students solving complex math problems, where the degree of collaboration is categorized at a high level. Data were collected
in the form of logs from students' tablets and the vocal interaction between pairs of students. Thousands of different features were defined, and then extracted computationally from the audio and log data. Human coders used richer data (several video streams) and a thorough understand of the tasks to code episodes as
collaborative, cooperative or asymmetric contribution. Machine learning was used to induce a detector, based on random forests, that outputs one of these three codes for an episode given only a characterization of the episode in terms of superficial features. An overall accuracy of 92.00% (kappa = 0.82) was obtained when
comparing the detector's codes to the humans' codes. However, due irregularities in running the study (e.g., the tablet software kept crashing), these results should be viewed as preliminary.
in the form of logs from students' tablets and the vocal interaction between pairs of students. Thousands of different features were defined, and then extracted computationally from the audio and log data. Human coders used richer data (several video streams) and a thorough understand of the tasks to code episodes as
collaborative, cooperative or asymmetric contribution. Machine learning was used to induce a detector, based on random forests, that outputs one of these three codes for an episode given only a characterization of the episode in terms of superficial features. An overall accuracy of 92.00% (kappa = 0.82) was obtained when
comparing the detector's codes to the humans' codes. However, due irregularities in running the study (e.g., the tablet software kept crashing), these results should be viewed as preliminary.
ContributorsViswanathan, Sree Aurovindh (Author) / VanLehn, Kurt (Thesis advisor) / T.H CHI, Michelene (Committee member) / Walker, Erin (Committee member) / Arizona State University (Publisher)
Created2014
Description
Categories are often defined by rules regarding their features. These rules may be intensely complex yet, despite the complexity of these rules, we are often able to learn them with sufficient practice. A possible explanation for how we arrive at consistent category judgments despite these difficulties would be that we may define these complex categories such as chairs, tables, or stairs by understanding the simpler rules defined by potential interactions with these objects. This concept, called grounding, allows for the learning and transfer of complex categorization rules if said rules are capable of being expressed in a more simple fashion by virtue of meaningful physical interactions. The present experiment tested this hypothesis by having participants engage in either a Rule Based (RB) or Information Integration (II) categorization task with instructions to engage with the stimuli in either a non-interactive or interactive fashion. If participants were capable of grounding the categories, which were defined in the II task with a complex visual rule, to a simpler interactive rule, then participants with interactive instructions should outperform participants with non-interactive instructions. Results indicated that physical interaction with stimuli had a marginally beneficial effect on category learning, but this effect seemed most prevalent in participants were engaged in an II task.
ContributorsCrawford, Thomas (Author) / Homa, Donald (Thesis advisor) / Glenberg, Arthur (Committee member) / McBeath, Michael (Committee member) / Brewer, Gene (Committee member) / Arizona State University (Publisher)
Created2014
Description
For this master's thesis, a unique set of cognitive prompts, designed to be delivered through a teachable robotic agent, were developed for students using Tangible Activities for Geometry (TAG), a tangible learning environment developed at Arizona State University. The purpose of these prompts is to enhance the affordances of the tangible learning environment and help researchers to better understand how we can design tangible learning environments to best support student learning. Specifically, the prompts explicitly encourage users to make use of their physical environment by asking students to perform a number of gestures and behaviors while prompting students about domain-specific knowledge. To test the effectiveness of these prompts that combine elements of cognition and physical movements, the performance and behavior of students who encounter these prompts while using TAG will be compared against the performance and behavior of students who encounter a more traditional set of cognitive prompts that would typically be used within a virtual learning environment. Following this study, data was analyzed using a novel modeling and analysis tool that combines enhanced log annotation using video and user model generation functionalities to highlight trends amongst students.
ContributorsThomas, Elissa (Author) / Burleson, Winslow (Thesis advisor) / Muldner, Katarzyna (Committee member) / Walker, Erin (Committee member) / Glenberg, Arthur (Committee member) / Arizona State University (Publisher)
Created2014
Description
Research in the learning sciences suggests that students learn better by collaborating with their peers than learning individually. Students working together as a group tend to generate new ideas more frequently and exhibit a higher level of reasoning. In this internet age with the advent of massive open online courses (MOOCs), students across the world are able to access and learn material remotely. This creates a need for tools that support distant or remote collaboration. In order to build such tools we need to understand the basic elements of remote collaboration and how it differs from traditional face-to-face collaboration.
The main goal of this thesis is to explore how spoken dialogue varies in face-to-face and remote collaborative learning settings. Speech data is collected from student participants solving mathematical problems collaboratively on a tablet. Spoken dialogue is analyzed based on conversational and acoustic features in both the settings. Looking for collaborative differences of transactivity and dialogue initiative, both settings are compared in detail using machine learning classification techniques based on acoustic and prosodic features of speech. Transactivity is defined as a joint construction of knowledge by peers. The main contributions of this thesis are: a speech corpus to analyze spoken dialogue in face-to-face and remote settings and an empirical analysis of conversation, collaboration, and speech prosody in both the settings. The results from the experiments show that amount of overlap is lower in remote dialogue than in the face-to-face setting. There is a significant difference in transactivity among strangers. My research benefits the computer-supported collaborative learning community by providing an analysis that can be used to build more efficient tools for supporting remote collaborative learning.
The main goal of this thesis is to explore how spoken dialogue varies in face-to-face and remote collaborative learning settings. Speech data is collected from student participants solving mathematical problems collaboratively on a tablet. Spoken dialogue is analyzed based on conversational and acoustic features in both the settings. Looking for collaborative differences of transactivity and dialogue initiative, both settings are compared in detail using machine learning classification techniques based on acoustic and prosodic features of speech. Transactivity is defined as a joint construction of knowledge by peers. The main contributions of this thesis are: a speech corpus to analyze spoken dialogue in face-to-face and remote settings and an empirical analysis of conversation, collaboration, and speech prosody in both the settings. The results from the experiments show that amount of overlap is lower in remote dialogue than in the face-to-face setting. There is a significant difference in transactivity among strangers. My research benefits the computer-supported collaborative learning community by providing an analysis that can be used to build more efficient tools for supporting remote collaborative learning.
ContributorsNelakurthi, Arun Reddy (Author) / Pon-Barry, Heather (Thesis advisor) / VanLehn, Kurt (Committee member) / Walker, Erin (Committee member) / Arizona State University (Publisher)
Created2014
Description
Learning and transfer were investigated for a categorical structure in which relevant stimulus information could be mapped without loss from one modality to another. The category space was composed of three non-overlapping, linearly-separable categories. Each stimulus was composed of a sequence of on-off events that varied in duration and number of sub-events (complexity). Categories were learned visually, haptically, or auditorily, and transferred to the same or an alternate modality. The transfer set contained old, new, and prototype stimuli, and subjects made both classification and recognition judgments. The results showed an early learning advantage in the visual modality, with transfer performance varying among the conditions in both classification and recognition. In general, classification accuracy was highest for the category prototype, with false recognition of the category prototype higher in the cross-modality conditions. The results are discussed in terms of current theories in modality transfer, and shed preliminary light on categorical transfer of temporal stimuli.
ContributorsFerguson, Ryan (Author) / Homa, Donald (Thesis advisor) / Goldinger, Stephen (Committee member) / Glenberg, Arthur (Committee member) / Arizona State University (Publisher)
Created2011
Description
The purpose of this study was to investigate the effect of partial exemplar experience on category formation and use. Participants had either complete or limited access to the three dimensions that defined categories by dimensions within different modalities. The concept of "crucial dimension" was introduced and the role it plays in category definition was explained. It was hypothesized that the effects of partial experience are not explained by a shifting of attention between dimensions (Taylor & Ross, 2009) but rather by an increased reliance on prototypical values used to fill in missing information during incomplete experiences. Results indicated that participants (1) do not fill in missing information with prototypical values, (2) integrate information less efficiently between different modalities than within a single modality, and (3) have difficulty learning only when partial experience prevents access to diagnostic information.
ContributorsCrawford, Thomas (Author) / Homa, Donald (Thesis advisor) / Mcbeath, Micheal (Committee member) / Glenberg, Arthur (Committee member) / Arizona State University (Publisher)
Created2011
Description
Lots of previous studies have analyzed human tutoring at great depths and have shown expert human tutors to produce effect sizes, which is twice of that produced by an intelligent tutoring system (ITS). However, there has been no consensus on which factor makes them so effective. It is important to know this, so that same phenomena can be replicated in an ITS in order to achieve the same level of proficiency as expert human tutors. Also, to the best of my knowledge no one has looked at student reactions when they are working with a computer based tutor. The answers to both these questions are needed in order to build a highly effective computer-based tutor. My research focuses on the second question. In the first phase of my thesis, I analyzed the behavior of students when they were working with a step-based tutor Andes, using verbal-protocol analysis. The accomplishment of doing this was that I got to know of some ways in which students use a step-based tutor which can pave way for the creation of more effective computer-based tutors. I found from the first phase of the research that students often keep trying to fix errors by guessing repeatedly instead of asking for help by clicking the hint button. This phenomenon is known as hint refusal. Surprisingly, a large portion of the student's foundering was due to hint refusal. The hypothesis tested in the second phase of the research is that hint refusal can be significantly reduced and learning can be significantly increased if Andes uses more unsolicited hints and meta hints. An unsolicited hint is a hint that is given without the student asking for one. A meta-hint is like an unsolicited hint in that it is given without the student asking for it, but it just prompts the student to click on the hint button. Two versions of Andes were compared: the original version and a new version that gave more unsolicited and meta-hints. During a two-hour experiment, there were large, statistically reliable differences in several performance measures suggesting that the new policy was more effective.
ContributorsRanganathan, Rajagopalan (Author) / VanLehn, Kurt (Thesis advisor) / Atkinson, Robert (Committee member) / Burleson, Winslow (Committee member) / Arizona State University (Publisher)
Created2011
Description
This study purposed to determine the effect of an endogenously designed instructional game on conceptual understanding of the associative and distributive properties of multiplication. Additional this study sought to investigate if performance on measures of conceptual understanding taken prior to and after game play could serve as predictors of game performance. Three versions of an instructional game, Shipping Express, were designed for the purposes of this study. The endogenous version of Shipping Express integrated the associative and distributive properties of multiplication within the mechanics, while the exogenous version had the instructional content separate from game play. A total of 111 fourth and fifth graders were randomly assigned to one of three conditions (endogenous, exogenous, and control) and completed pre and posttest measures of conceptual understanding of the associative and distributive properties of multiplication, along with a questionnaire. The results revealed several significant results: 1) there was a significant difference between participants' change in scores on the measure of conceptual understanding of the associative property of multiplication, based on the version of Shipping Express they played. Participants who played the endogenous version of Shipping Express had on average higher gains in scores on the measure of conceptual understanding of the associative property of multiplication than those who played the other versions of Shipping Express; 2) performance on the measures of conceptual understanding of the distributive property collected prior to game play were related to performance within the endogenous game environment; and 3) participants who played the control version of Shipping Express were on average more likely to have a negative attitude towards continuing game play on their own compared to the other versions of the game. No significant differences were found in regards to changes in scores on the measure of conceptual understanding of the distributive property based on the version of Shipping Express played, post hoc pairwise comparisons, and changes on scores on question types within the conceptual understanding of the associative and distributive property of multiplication measures. The findings from this study provide some support for a move towards the design and development of endogenous instructional games. Additional implications for the learning through digital game play and future research directions are discussed.
ContributorsDenham, Andrew (Author) / Nelson, Brian C. (Thesis advisor) / Atkinson, Robert K. (Committee member) / Middleton, James (Committee member) / VanLehn, Kurt (Committee member) / Arizona State University (Publisher)
Created2012
Description
The mathematics test is the most difficult test in the GED (General Education Development) Test battery, largely due to the presence of story problems. Raising performance levels of story problem-solving would have a significant effect on GED Test passage rates. The subject of this formative research study is Ms. Stephens’ Categorization Practice Utility (MS-CPU), an example-tracing intelligent tutoring system that serves as practice for the first step (problem categorization) in a larger comprehensive story problem-solving pedagogy that purports to raise the level of story problem-solving performance. During the analysis phase of this project, knowledge components and particular competencies that enable learning (schema building) were identified. During the development phase, a tutoring system was designed and implemented that algorithmically teaches these competencies to the student with graphical, interactive, and animated utilities. Because the tutoring system provides a much more concrete rather than conceptual, learning environment, it should foster a much greater apprehension of a story problem-solving process. With this experience, the student should begin to recognize the generalizability of concrete operations that accomplish particular story problem-solving goals and begin to build conceptual knowledge and a more conceptual approach to the task. During the formative evaluation phase, qualitative methods were used to identify obstacles in the MS-CPU user interface and disconnections in the pedagogy that impede learning story problem categorization and solution preparation. The study was conducted over two iterations where identification of obstacles and change plans (mitigations) produced a qualitative data table used to modify the first version systems (MS-CPU 1.1). Mitigation corrections produced the second version of the MS-CPU 1.2, and the next iteration of the study was conducted producing a second set of obstacle/mitigation tables. Pre-posttests were conducted in each iteration to provide corroboration for the effectiveness of the mitigations that were performed. The study resulted in the identification of a number of learning obstacles in the first version of the MS-CPU 1.1. Their mitigation produced a second version of the MS-CPU 1.2 whose identified obstacles were much less than the first version. It was determined that an additional iteration is needed before more quantitative research is conducted.
ContributorsRitchey, ChristiAnne (Author) / VanLehn, Kurt (Thesis advisor) / Savenye, Wilhelmina (Committee member) / Hong, Yi-Chun (Committee member) / Arizona State University (Publisher)
Created2018