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Nagashima, Tomohiro; Bartel, Anna N.; Yadav, Gautam; Tseng, Stephanie; Vest, Nicholas A.; Silla, Elena M.; Alibali, Martha W.; Aleven, Vincent – Grantee Submission, 2021
Prior research shows that self-explanation promotes understanding by helping learners connect new knowledge with prior knowledge. However, despite ample evidence supporting the effectiveness of self-explanation, an instructional design challenge emerges in how best to scaffold self-explanation. In particular, it is an open challenge to design…
Descriptors: Teaching Methods, Mathematics Instruction, Algebra, Middle School Students
Bartel, Anna N.; Silla, Elena M.; Vest, Nicholas A.; Nagashima, Tomohiro; Aleven, Vincent; Alibali, Martha W. – Grantee Submission, 2021
Research has shown that tape diagrams are beneficial for algebra learning. However, it is unclear whether certain visual features of tape diagrams have implications for learning. We investigated, with undergraduate students and math teachers, whether tape diagrams with different visual features (color, presence of outer lines, and position of the…
Descriptors: Mathematics Skills, Visual Aids, Algebra, Undergraduate Students
Nagashima, Tomohiro; Yang, Kexin; Bartel, Anna N.; Silla, Elena M.; Vest, Nicholas A.; Alibali, Martha W.; Aleven, Vincent – Grantee Submission, 2020
When designing an instructional tool and using it in pedagogical activities, it is essential that designers and users understand what pedagogical affordances and constraints the tool provides to support its successful integration into targeted pedagogical activities. Toward this end, we developed "Pedagogical Affordance Analysis (PAA)."…
Descriptors: Teaching Methods, Pedagogical Content Knowledge, Mathematics Instruction, Middle School Students
Nagashima, Tomohiro; Bartel, Anna N.; Silla, Elena M.; Vest, Nicholas A.; Alibali, Martha W.; Aleven, Vincent – Grantee Submission, 2020
Many studies have shown that visual representations can enhance student understanding of STEM concepts. However, prior research suggests that visual representations alone are not necessarily effective across a broad range of students. To address this problem, we created a novel, scaffolded form of diagrammatic self-explanation in which students…
Descriptors: Algebra, Teaching Methods, Visual Aids, Concept Formation
Fancsali, Stephen E.; Holstein, Kenneth; Sandbothe, Michael; Ritter, Steven; McLaren, Bruce M.; Aleven, Vincent – Grantee Submission, 2020
Extensive literature in artificial intelligence in education focuses on developing automated methods for detecting cases in which students struggle to master content while working with educational software. Such cases have often been called "wheel-spinning," "unproductive persistence," or "unproductive struggle." We…
Descriptors: Artificial Intelligence, Automation, Persistence, Intelligent Tutoring Systems
Holstein, Kenneth; McLaren, Bruce M.; Aleven, Vincent – Grantee Submission, 2019
Involving stakeholders throughout the creation of new educational technologies can help ensure their usefulness and usability in real-world contexts. However, given the complexity of learning analytics (LA) systems, it can be challenging to meaningfully involve non-technical stakeholders throughout their design and development. This article…
Descriptors: Learning Analytics, Technology Uses in Education, Artificial Intelligence, Stakeholders
Zhang, Chuankai; Huang, Yanzun; Wang, Jingyu; Lu, Dongyang; Fang, Weiqi; Stamper, John; Fancsali, Stephen; Holstein, Kenneth; Aleven, Vincent – Grantee Submission, 2019
"Wheel spinning" is the phenomenon in which a student fails to master a Knowledge Component (KC), despite significant practice. Ideally, an intelligent tutoring system would detect this phenomenon early, so that the system or a teacher could try alternative instructional strategies. Prior work has put forward several criteria for wheel…
Descriptors: Identification, Intelligent Tutoring Systems, Academic Failure, Criteria
Holstein, Kenneth; McLaren, Bruce M.; Aleven, Vincent – Grantee Submission, 2019
As artificial intelligence (AI) increasingly enters K-12 classrooms, what do teachers and students see as the roles of human versus AI instruction, and how might educational AI (AIED) systems best be designed to support these complementary roles? We explore these questions through participatory design and needs validation studies with K12 teachers…
Descriptors: Artificial Intelligence, Intelligent Tutoring Systems, Instructional Design, Elementary Secondary Education
Hartmann, Christian; Olsen, Jennifer K.; Brand, Charleen; Aleven, Vincent; Rummel, Nikol – Grantee Submission, 2017
Social interdependence is a key concept in CSCL research. However, investigations of students' positive and negative interdependence during collaborative activities have often relied on self-report, rather than dialogue analysis. Bringing together politeness and social interdependence theory, we assessed "dialogue indicators" of positive…
Descriptors: Elementary School Students, Computer Uses in Education, Cooperative Learning, Intelligent Tutoring Systems
Olsen, Jennifer K.; Rummel, Nikol; Aleven, Vincent – Grantee Submission, 2017
Collaborative and individual learning are both frequently used in classrooms to support learning. However, little research has investigated the benefits of combining individual and collaborative learning, as compared to learning only individually or only collaboratively. With our study, we address this research gap. We compared a combined…
Descriptors: Cooperative Learning, Grade 4, Grade 5, Elementary School Students
Doroudi, Shayan; Aleven, Vincent; Brunskill, Emma – Grantee Submission, 2017
The gold standard for identifying more effective pedagogical approaches is to perform an experiment. Unfortunately, frequently a hypothesized alternate way of teaching does not yield an improved effect. Given the expense and logistics of each experiment, and the enormous space of potential ways to improve teaching, it would be highly preferable if…
Descriptors: Teaching Methods, Matrices, Evaluation Methods, Models
Rau, Martina A.; Aleven, Vincent; Rummel, Nikol – Grantee Submission, 2017
Prior research shows that representational competencies that enable students to use graphical representations to reason and solve tasks is key to learning in many science, technology, engineering, and mathematics (STEM) domains. We focus on two types of representational competencies: (1) "sense making" of connections by verbally…
Descriptors: Elementary School Students, Grade 3, Grade 4, Grade 5
Rau, Martina A.; Aleven, Vincent; Rummel, Nikol – Grantee Submission, 2017
Prior research shows that multiple representations can enhance learning, provided that students make connections among them. We hypothesized that support for connection making is most effective in enhancing learning of domain knowledge if it helps students both in making sense of these connections and in becoming perceptually fluent in making…
Descriptors: Elementary School Students, Grade 4, Grade 5, Comprehension
Olsen, Jennifer K.; Aleven, Vincent; Rummel, Nikol – Grantee Submission, 2017
In working towards unraveling the mechanisms of productive collaborative learning, dual eye tracking is a potentially helpful methodology. Dual eye tracking is a method where eye-tracking data from people working on a task are analyzed jointly, for example to extract measures of joint visual attention. We explore how eye gaze relates to effective…
Descriptors: Eye Movements, Cooperation, Communication (Thought Transfer), Outcomes of Education
Olsen, Jennifer K.; Rummel, Nikol; Aleven, Vincent – Grantee Submission, 2016
Intelligent Tutoring Systems (ITSs) are beneficial for individual students learning in several domains, including mathematics where they have been used to support both secondary and elementary students. Collaborative learning may be beneficial to include in ITSs, particularly for conceptual knowledge. There is little work on collaborative ITSs,…
Descriptors: Intelligent Tutoring Systems, Cooperative Learning, Elementary School Students, Elementary School Mathematics
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