Luis P Prieto
Biographic Data
| ID | 6605787 |
|---|---|
| NAME | Luis P Prieto |
| GIVEN NAMES | Luis P |
| FAMILY NAME | Prieto |
| SIGNATURE | PRIETO L P |
| AFFILIATIONS | Tallinn University |
| ORCID | 0000-0002-0057-0682 |
| VERIFIED | Yes |
| TOTAL WORKS | 9 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 9 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2014 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Monitoring and evaluating school-university partnerships: Insights from the usability assessment and refinement of a conceptual framework
School-university partnerships (SUPs) are collaborations bridging the expertise of school and university representatives to address problems of educational practice. A critical and yet challenging aspect of SUPs is their monitoring and evaluation. There exist only a few monitoring and evaluation tools, nevertheless, they only partially address the complexity of SUPs, and some of them have limited adoption in practice. The SUP.ME framework was dev…
Aligning human values and educational technologies with value‐sensitive design
Contains fulltext : 319251.pdf (Publisher’s version ) (Open Access)
What kind of learning designs do practitioners create for mobile learning? Lessons learnt from two in‐the‐wild case studies
How well do collaboration quality estimation models generalize across authentic school contexts
Multimodal learning analytics (MMLA) research has made significant progress in modelling collaboration quality for the purpose of understanding collaboration behaviour and building automated collaboration estimation models. Deploying these automated models in authentic classroom scenarios, however, remains a challenge. This paper presents findings from an evaluation of collaboration quality estimation models. We collected audio, video and log dat…
Teacher Artificial Intelligence-Supported Pedagogical Actions in Collaborative Learning Coregulation: A Wizard-of-Oz Study
Orchestrating collaborative learning (CL) is difficult for teachers as it involves being aware of multiple simultaneous classroom events and intervening when needed. Artificial intelligence (AI) technology might support the teachers’ pedagogical actions during CL by helping detect students in need and providing suggestions for intervention. This would be resulting in AI and teacher co-orchestrating CL; the effectiveness of which, however, is stil…
Learning design and learning analytics in mobile and ubiquitous learning: A systematic review
Mobile and Ubiquitous Learning (m/u‐learning) are finding an increasing adoption in education. They are often distinguished by hybrid learning environments that encompass elements of formal and informal learning, in activities that happen in distributed settings (indoors and outdoors), across physical and virtual spaces. Despite their purported benefits, these environments imply additional complexity in the design, monitoring and evaluation of le…
Orchestrating learning analytics (OrLA): Supporting inter-stakeholder communication about adoption of learning analytics at the classroom level
Despite the recent surge of interest in learning analytics (LA), their adoption in everyday classroom practice is still slow. Knowledge gaps and lack of inter-stakeholder communication (particularly with educational practitioners) have been posited as critical factors for previous LA adoption failures. Yet, what issues should researchers, practitioners and other actors communicate about, when considering the adoption of an LA innovation in a part…
Perceiving Learning at a Glance: A Systematic Literature Review of Learning Dashboard Research
Orchestrating Evaluation of Complex Educational Technologies: A Case Study of a CSCL System
As digital technologies permeate every aspect of our lives, the complexity of the educational settings, and of the technological support we use within them, unceasingly rises. This increased complexity, along with the need for educational practitioners to apply such technologies within multi-constraint authentic settings, has given rise to the notion of technology-enhanced learning practice as “orchestration of learning”. However, at the same tim…
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Orchestrating Evaluation of Complex Educational Technologies: A Case Study of a CSCL System
As digital technologies permeate every aspect of our lives, the complexity of the educational settings, and of the technological support we use within them, unceasingly rises. This increased complexity, along with the need for educational practitioners to apply such technologies within multi-constraint authentic settings, has given rise to the notion of technology-enhanced learning practice as “orchestration of learning”. However, at the same tim…
Perceiving Learning at a Glance: A Systematic Literature Review of Learning Dashboard Research
Orchestrating learning analytics (OrLA): Supporting inter-stakeholder communication about adoption of learning analytics at the classroom level
Despite the recent surge of interest in learning analytics (LA), their adoption in everyday classroom practice is still slow. Knowledge gaps and lack of inter-stakeholder communication (particularly with educational practitioners) have been posited as critical factors for previous LA adoption failures. Yet, what issues should researchers, practitioners and other actors communicate about, when considering the adoption of an LA innovation in a part…
Learning design and learning analytics in mobile and ubiquitous learning: A systematic review
Mobile and Ubiquitous Learning (m/u‐learning) are finding an increasing adoption in education. They are often distinguished by hybrid learning environments that encompass elements of formal and informal learning, in activities that happen in distributed settings (indoors and outdoors), across physical and virtual spaces. Despite their purported benefits, these environments imply additional complexity in the design, monitoring and evaluation of le…
Teacher Artificial Intelligence-Supported Pedagogical Actions in Collaborative Learning Coregulation: A Wizard-of-Oz Study
Orchestrating collaborative learning (CL) is difficult for teachers as it involves being aware of multiple simultaneous classroom events and intervening when needed. Artificial intelligence (AI) technology might support the teachers’ pedagogical actions during CL by helping detect students in need and providing suggestions for intervention. This would be resulting in AI and teacher co-orchestrating CL; the effectiveness of which, however, is stil…
What kind of learning designs do practitioners create for mobile learning? Lessons learnt from two in‐the‐wild case studies
How well do collaboration quality estimation models generalize across authentic school contexts
Multimodal learning analytics (MMLA) research has made significant progress in modelling collaboration quality for the purpose of understanding collaboration behaviour and building automated collaboration estimation models. Deploying these automated models in authentic classroom scenarios, however, remains a challenge. This paper presents findings from an evaluation of collaboration quality estimation models. We collected audio, video and log dat…
Monitoring and evaluating school-university partnerships: Insights from the usability assessment and refinement of a conceptual framework
School-university partnerships (SUPs) are collaborations bridging the expertise of school and university representatives to address problems of educational practice. A critical and yet challenging aspect of SUPs is their monitoring and evaluation. There exist only a few monitoring and evaluation tools, nevertheless, they only partially address the complexity of SUPs, and some of them have limited adoption in practice. The SUP.ME framework was dev…
Aligning human values and educational technologies with value‐sensitive design
Contains fulltext : 319251.pdf (Publisher’s version ) (Open Access)
Computer Science (7 works) · Knowledge management (5 works) · Online and Blended Learning (5 works) · Online Learning and Analytics (4 works) · Psychology (4 works) · Data science (3 works) · Engineering (3 works) · Innovative Teaching and Learning Methods (3 works) · Analytics (2 works) · Artificial Intelligence (2 works)