Better Understanding Peoples’ Experiences with Technologically Mediated Learning, What Works, for Whom, and within What Situations: Large-Scale Analytics of Learning Experiences and Outcomes to Inform Practice
This study sought to understand variations in student experiences of technologically mediated learning in the Fresno Unified School District, facilitated by its personalized learning and innovations program (PLI) and large-scale data models.
While evidence supports the intentional use of information and communication technology (ICT) in learning, the focus often neglects students’ social conditions, situatedness, and self-determination.
A growth mixture model explored students’ learning and revealed four distinct learning patterns: accelerating, decelerating, languishing, and thriving.
During pandemic-era remote and hybrid teaching, this study applied Growth Mixture Modeling to identify learning trajectories among 15,135 Grade 4–6 students in a large urban school district. The research revealed how social conditions, situatedness, and self-determination shaped learning outcomes by integrating real-time digital engagement data, assessment results, and student demographics. The findings offer practical insights for educators to personalize instruction using timely data signals and highlight how educational systems can build analytics capacity to understand better what works, for whom, and in which contexts. This work advances both research and practice by combining rigorous methodology with actionable strategies for equity-focused teaching and future-ready learning.
Data suggest the shift to remote teaching was harder for those most adept with blended instructional models, while increased support for special education and English language learners facilitated learning.
Educators can use near-real-time data to inform teaching practices that personalize student support and learning experiences while cultivating students’ self-determination. Such data includes formative assessments, checks for understanding, observing writing in process, and real-time signals from interactive learning platforms, allowing students, their peers, and educators to provide appropriate and effective supports. Educational leaders can determine what interventions work better for various student groups and focus investments on high-impact programmatic interventions.
Leverage the use of traditional data combined with novel signals of students’ learning experiences to explore what works better for various student groups, social conditions, and situatedness. “Situatedness” reflects how technological acceptance and use are situated within the social context, social interactions, across academic content areas, and grounded in space. “Social conditions” include socio-economic status, family systems, neighborhoods, as well as classroom and school cultures.
The use of near-real-time data signals from students’ learning experiences supports differentiated and engaging instruction, personalized learning, and enables educators to better support students’ growth in self-determination. These teacher practices better prepare students with the knowledge, skills, values, and attitudes to be ready for their futures. As demonstrated herein, education analytics can support faster improvement cycles within educational systems.
Future research might include: (1) qualitative methods to gather students’ perspectives on their learning experiences, and (2) exploration of the relationship between the quality of students’ internet access and its effects on student learning experiences.
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