Danial Hooshyar
Biographic Data
| ID | 9510276 |
|---|---|
| NAME | Danial Hooshyar |
| GIVEN NAMES | Danial |
| FAMILY NAME | Hooshyar |
| SIGNATURE | HOOSHYAR D |
| AFFILIATIONS | Tallinn University |
| ORCID | 0000-0002-9143-6648 |
| VERIFIED | Yes |
| TOTAL WORKS | 8 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 8 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Tracing and supporting self-regulated learning in K-12 digital learning
Learning algebra
Learning difficulties affect the acquisition of mathematics, making it challenging to keep up with peers. Topics in algebra require new conceptual knowledge and symbols due to its abstract nature. This study explored the similarities and differences among students with reading difficulties, mathematics difficulties, low arithmetic skill (as identified by researchers) and students without learning difficulties. A total of 773 eighth-grade students…
SRL Components Predicting Math Results in Estonian Middle School
Self‐regulated learning (SRL) components, such as motivation, cognitive skills and learning strategies, play a critical role in mathematics (math) learning. Using Bayesian Networks (BN), this study examines which SRL components predict membership in high (75th percentile) and low‐performing (25th percentile) groups in factual/procedural and conceptual math among middle school students ( N = 658). The results revealed that cognitive skills—specifi…
Differentiated learning materials—Effects on mathematics knowledge and motivation
Differentiation is a widely used concept that supports inclusive education. This study aims to explore the differences in students’ mathematics knowledge and motivation when learning with and without differentiated learning materials, while also comparing the effect of pen-and-paper and digital differentiation. The sample comprised 321 Estonian students from 8th grade. A quasi-experimental pre-test–post-test design was applied. While students lea…
Memory-Based Dynamic Bayesian Networks for Learner Modeling
Artificial intelligence (AI) has demonstrated significant potential in addressing educational challenges in digital learning. Despite this potential, there are still concerns about the interpretability and trustworthiness of AI methods. Dynamic Bayesian networks (DBNs) not only provide interpretability and the ability to integrate data-driven insights with expert judgment for enhanced trustworthiness but also effectively process temporal dynamics…
The effectiveness of personalized technology-enhanced learning in higher education
An adaptive educational computer game
Open learner models in supporting self-regulated learning in higher education
No prominent works on this page.
Open learner models in supporting self-regulated learning in higher education
An adaptive educational computer game
Memory-Based Dynamic Bayesian Networks for Learner Modeling
Artificial intelligence (AI) has demonstrated significant potential in addressing educational challenges in digital learning. Despite this potential, there are still concerns about the interpretability and trustworthiness of AI methods. Dynamic Bayesian networks (DBNs) not only provide interpretability and the ability to integrate data-driven insights with expert judgment for enhanced trustworthiness but also effectively process temporal dynamics…
The effectiveness of personalized technology-enhanced learning in higher education
Tracing and supporting self-regulated learning in K-12 digital learning
Learning algebra
Learning difficulties affect the acquisition of mathematics, making it challenging to keep up with peers. Topics in algebra require new conceptual knowledge and symbols due to its abstract nature. This study explored the similarities and differences among students with reading difficulties, mathematics difficulties, low arithmetic skill (as identified by researchers) and students without learning difficulties. A total of 773 eighth-grade students…
SRL Components Predicting Math Results in Estonian Middle School
Self‐regulated learning (SRL) components, such as motivation, cognitive skills and learning strategies, play a critical role in mathematics (math) learning. Using Bayesian Networks (BN), this study examines which SRL components predict membership in high (75th percentile) and low‐performing (25th percentile) groups in factual/procedural and conceptual math among middle school students ( N = 658). The results revealed that cognitive skills—specifi…
Differentiated learning materials—Effects on mathematics knowledge and motivation
Differentiation is a widely used concept that supports inclusive education. This study aims to explore the differences in students’ mathematics knowledge and motivation when learning with and without differentiated learning materials, while also comparing the effect of pen-and-paper and digital differentiation. The sample comprised 321 Estonian students from 8th grade. A quasi-experimental pre-test–post-test design was applied. While students lea…
Computer Science (4 works) · Online Learning and Analytics (4 works) · Psychology (4 works) · Innovative Teaching and Learning Methods (3 works) · Mathematics Education and Teaching Techniques (3 works) · Artificial Intelligence (2 works) · Knowledge level (2 works) · Mathematics education (2 works) · Teaching method (2 works) · Active learning (machine learning) (1 work)