Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Affect (3)
- Bayesian Knowledge Tracing (2)
- Educational games (2)
- Intelligent Tutoring Systems (2)
- Intelligent tutoring system (2)
-
- Intelligent tutoring systems (2)
- Affective computing (1)
- Affective dynamics (1)
- Affective persistence (1)
- Bayesian Knowledge-Tracing (1)
- Bayesian Networks (1)
- Behavior detection (1)
- Carelessness (1)
- Cognitive Modeling (1)
- Cognitive Tutor (1)
- Cognitive-affective states (1)
- Data mining (1)
- Designing and conducting experiments (1)
- Difficulty Factors Assessments (1)
- Educational Data Mining (1)
- Educational Data Mining. (1)
- Educational data mining (1)
- Embodied Conversational Agent (1)
- Evaluation of AIED Systems (1)
- Exploratory learning environment assessment (1)
- Gaming the system (1)
- IRT (1)
- Instructional Design Principles (1)
- Intelligent Tutoring System (1)
- Learner models (1)
Articles 1 - 10 of 10
Full-Text Articles in Engineering
Population Validity For Educational Data Mining Models: A Case Study In Affect Detection, Ryan Baker, Jaclyn Ocumpaugh, Sujith Gowda, Neil Heffermnan, Cristina Heffernan
Population Validity For Educational Data Mining Models: A Case Study In Affect Detection, Ryan Baker, Jaclyn Ocumpaugh, Sujith Gowda, Neil Heffermnan, Cristina Heffernan
Ryan S.J.d. Baker
ICT-enhanced research methods such as educational data mining (EDM) have allowed researchers to effectively model a broad range of constructs pertaining to the student, moving from traditional assessments of knowledge to assessment of engagement, meta-cognition, strategy, and affect. The automated detection of these constructs allows EDM researchers to develop intervention strategies that can be implemented either by the software or the teacher. It also allows for secondary analyses of the construct, where the detectors are applied to a data set that is much larger than one that could be analyzed by more traditional methods. However, in many cases, the data …
Carelessness And Affect In An Intelligent Tutoring System For Mathematics, Ryan Baker, Ma.Mercedes Rodrigo, Maria Ofelia San Pedro
Carelessness And Affect In An Intelligent Tutoring System For Mathematics, Ryan Baker, Ma.Mercedes Rodrigo, Maria Ofelia San Pedro
Ryan S.J.d. Baker
We investigate the relationship between students’ affect and their frequency of careless errors while using an Intelligent Tutoring System for middle school mathematics. A student is said to have committed a careless error when the student’s answer is wrong despite knowing the skill required to provide the correct answer. We operationalize the probability that an error is careless through the use of an automated detector, developed using educational data mining, which infers the probability that an error involves carelessness rather than not knowing the relevant skill. This detector is then applied to log data produced by high-school students in the …
Leveraging Machine-Learned Detectors Of Systematic Inquiry Behavior To Estimate And Predict Transfer Of Inquiry Skill, Ryan Baker, Michael Sao Pedro, Janice Gobert, Orlando Montalvo, Adam Nakama
Leveraging Machine-Learned Detectors Of Systematic Inquiry Behavior To Estimate And Predict Transfer Of Inquiry Skill, Ryan Baker, Michael Sao Pedro, Janice Gobert, Orlando Montalvo, Adam Nakama
Ryan S.J.d. Baker
We present work toward automatically assessing and estimating science inquiry skills as middle school students engage in inquiry within a physical science microworld. Towards accomplishing this goal, we generated machine‐learned models that can detect when students test their articulated hypotheses, design controlled experiments, and engage in planning behaviors using two inquiry support tools. Models were trained using labels generated through a new method of manually hand‐coding log files, “text replay tagging”. This approach led to detectors that can automatically and accurately identify these inquiry skills under student‐level cross‐validation. The resulting detectors can be applied at run‐time to drive scaffolding intervention. …
Towards Automatically Detecting Whether Student Learning Is Shallow, Ryan Baker, Sujith Gowda, Albert Corbett, Lisa Rossi
Towards Automatically Detecting Whether Student Learning Is Shallow, Ryan Baker, Sujith Gowda, Albert Corbett, Lisa Rossi
Ryan S.J.d. Baker
Recent research has extended student modeling to infer not just whether a student knows a skill or set of skills, but also whether the student has achieved robust learning – learning that enables the student to transfer their knowledge and prepares them for future learning (PFL). However, a student may fail to have robust learning in two fashions: they may have no learning, or they may have shallow learning (learning that applies only to the current skill, and does not support transfer or PFL). Within this paper, we present automated detectors which identify shallow learners, who are likely to need …
A Review Of Recent Advances In Learner And Skill Modeling In Intelligent Learning Environments, Ryan Baker, Michael Desmaris
A Review Of Recent Advances In Learner And Skill Modeling In Intelligent Learning Environments, Ryan Baker, Michael Desmaris
Ryan S.J.d. Baker
In recent years, learner models have emerged from the research laboratory and research classrooms into the wider world. Learner models are now embedded in real world applications which can claim to have thousands, or even hundreds of thousands, of users. Probabilistic models for skill assessment are playing a key role in these advanced learning environments. In this paper, we review the learner models that have played the largest roles in the success of these learning environments, and also the latest advances in the modeling and assessment of learner skills. We conclude by discussing related advancements in modeling other key constructs …
The Effects Of An Interactive Software Agent On Student Affective Dynamics While Using An Intelligent Tutoring System, Ryan Baker, Ma.Mercedes Rodrigo, Jenilyn Agapito, Julieta Nabos, Ma.Concepcion Repalam, Salvador Reyes, Maria Ofelia San Pedro
The Effects Of An Interactive Software Agent On Student Affective Dynamics While Using An Intelligent Tutoring System, Ryan Baker, Ma.Mercedes Rodrigo, Jenilyn Agapito, Julieta Nabos, Ma.Concepcion Repalam, Salvador Reyes, Maria Ofelia San Pedro
Ryan S.J.d. Baker
We study the affective states exhibited by students using an intelligent tutoring system for Scatterplots with and without an interactive software agent, Scooter the Tutor. Scooter the Tutor had been previously shown to lead to improved learning outcomes as compared to the same tutoring system without Scooter. We found that affective states and transitions between affective states were very similar among students in both conditions. With the exception of the “neutral state”, no affective state occurred significantly more in one condition over the other. Boredom, confusion, and engaged concentration persisted in both conditions, representing both “virtuous cycles” and “vicious cycles” …
Detecting Learning Moment-By-Moment, Ryan Baker, Adam Goldstein, Neil Heffernan
Detecting Learning Moment-By-Moment, Ryan Baker, Adam Goldstein, Neil Heffernan
Ryan S.J.d. Baker
Intelligent tutors have become increasingly accurate at detecting whether a student knows a skill, or knowledge component (KC), at a given time. However, current student models do not tell us exactly at which point a KC is learned. In this paper, we present a machine-learned model that assesses the probability that a student learned a KC at a specific problem step (instead of at the next or previous problem step). We use this model to analyze which KCs are learned gradually, and which are learned in “eureka” moments. We also discuss potential ways that this model could be used to …
Comparing Learners' Affect While Using An Intelligent Tutor And An Educational Game, Ryan Baker, Ma.Mercedes Rodrigo
Comparing Learners' Affect While Using An Intelligent Tutor And An Educational Game, Ryan Baker, Ma.Mercedes Rodrigo
Ryan S.J.d. Baker
We compare the affect associated with students learning from an intelligent tutoring system, Aplusix, and a game, Math Blaster 9-12, covering very similar mathematical content. Quantitative field observations of student affect were conducted in classrooms in private schools in the Philippines. Students experienced large amounts of positive affect in both environments. It has been hypothesized that educational games will lead to better affect than other forms of educational software, but it was found that students experienced more positive affect (specifically, engaged concentration) and less negative affect (specifically, boredom) in the intelligent tutor than in the game, though there was a …
Better To Be Frustrated Than Bored: The Incidence, Persistence, And Impact Of Learners' Cognitive-Affective States During Interactions With Three Different Computer-Based Learning Environments, Ryan Baker, Ma.Mercedes Rodrigo, Sidney D'Mello, Arthur Graesser
Better To Be Frustrated Than Bored: The Incidence, Persistence, And Impact Of Learners' Cognitive-Affective States During Interactions With Three Different Computer-Based Learning Environments, Ryan Baker, Ma.Mercedes Rodrigo, Sidney D'Mello, Arthur Graesser
Ryan S.J.d. Baker
We study the incidence (rate of occurrence), persistence (rate of reoccurrence immediately after occurrence), and impact (effect on behavior) of students’ cognitive-affective states during their use of three different computer-based learning environments. Students’ cognitive-affective states are studied using different populations (Philippines, USA), different methods (quantitative field observation, self-report), and different types of learning environments (dialogue tutor, problemsolving game, and problem-solving based Intelligent Tutoring System). By varying the studies along these multiple factors, we can have greater confidence that findings which generalize across studies are robust. The incidence, persistence, and impact of boredom, frustration, confusion, engaged concentration, delight, and surprise were …
The Difficulty Factors Approach To The Design Of Lessons In Intelligent Tutor Curricula, Ryan Baker, Albert Corbett, Kenneth Koedinger
The Difficulty Factors Approach To The Design Of Lessons In Intelligent Tutor Curricula, Ryan Baker, Albert Corbett, Kenneth Koedinger
Ryan S.J.d. Baker
We present an approach to designing intelligent tutoring systems, termed the Difficulty Factors Approach. In this approach, the designer investigates, at each iteration of the design cycle, which skills and concepts are difficult for students, and what factors underlie those difficulties. We show that this approach complements existing design principles, producing data that helps designers apply principles in context. We also show that by continuing to investigate student difficulties throughout the design process, it is possible to discover difficulty factors initially obscured by other difficulty factors. We give an example of the application of the Difficulty Factors Approach in the …