Complex dynamic systems offer a rich platform for understanding the individual or the person‐specific mechanisms. Yet, in learning analytics research and education at large, a complex dynamic system ...has rarely been framed, developed, or used to understand the individual student where the learning process takes place. Individual (or person‐specific) methods can accurately and precisely model the individual person, create person‐specific models, and devise unique parameters for each individual. Our study used the latest advances in complex systems dynamics to study the differences between group‐based and individual self‐regulated learning (SRL) dynamics. The findings show that SRL is a complex, dynamic system where different sub‐processes influence each other resulting in the emergence of non‐trivial patterns that vary across individuals and time scales, and as such far from the uniform picture commonly theorized. We found that the average SRL process does not reflect the individual SRL processes of different people. Therefore, interventions derived from the group‐based SRL insights are unlikely to be effective in personalization. We posit that, if personalized interventions are needed, modelling the person with person‐specific methods should be the guiding principle. Our study offered a reliable solution to model the person‐specific self‐regulation processes which can serve as a ground for understanding and improving individual learning and open the door for precision education.
Practitioner notes
What is already known about this topic
Self‐regulation is a catalyst for effective learning and achievement.
Our understanding of SRL personalization comes from insights based on aggregate group‐based data.
What this paper adds
Every student has their own unique SRL process that varies from the average in non‐trivial ways.
We offer a credible method for mapping the individualized SRL process.
SRL dynamics vary across time scales. That is, the trait dynamics are different from the state dynamics, and they should be supported differently.
Implications for practice and/or policy
Personalization can best be achieved if based on unique person‐specific idiographic methods.
Supporting learning and SRL in particular can be more efficient when we understand the differences across time scales and persons and apply insights accordingly.
The general SRL average should not be expected to work for everyone.
Student engagement has a trajectory (a timeline) that unfolds over time and can be shaped by different factors including learners’ motivation, school conditions, and the nature of learning tasks. ...Such factors may result in either a stable, declining or fluctuating engagement trajectory. While research on online engagement is abundant, most authors have examined student engagement in a single course or two. Little research has been devoted to studying online longitudinal engagement, i.e., the evolution of student engagement over a full educational program. This learning analytics study examines the engagement states (sequences, successions, stability, and transitions) of 106 students in 1396 course enrollments over a full program. All data of students enrolled in the academic year 2014–2015, and their subsequent data in 2015–2016, 2016–2017, and 2017–2018 (15 courses) were collected. The engagement states were clustered using Hidden Markov Models (HMM) to uncover the hidden engagement trajectories which resulted in a mostly-engaged (33% of students), an intermediate (39.6%), and a troubled (27.4%) trajectory. The mostly-engaged trajectory was stable with infrequent changes, scored the highest, and was less likely to drop out. The troubled trajectory showed early disengagement, frequent dropouts and scored the lowest grades. The results of our study show how to identify early program disengagement (activities within the third decile) and when students may drop out (first year and early second year).
•Three trajectories of engagement were identified: a Mostly-engaged, an Intermediate and a Troubled trajectory.•The Mostly-engaged trajectory showed consistent engagement, higher scores and better graduation rates.•The troubled trajectory showed early signs of disengagement, frequent dropouts and lower scores.•Longitudinal engagement predicts graduation and academic performance.•We provide a novel method for studying longitudinal sequence of engagement states, transitions, and engagement trajectories.
The use of digitial twins (DTs) in industry has become a growing trend in recent years, allowing improvement of the life cycle of any process by taking advantage of the relationship between the ...physical and virtual worlds. Existing literature posits several challenges for building DTs, as well as some proposals for overcoming them. However, in the vast majority of the cases, the architectures and technologies presented are strongly bounded to the domain where the DTs are applied. This article proposes the FIWARE Ecosystem, combining its catalog of components and smart data models as a solution for the development of any DT. We also provide a use case to show how to use FIWARE for building DTs through a complete example of a parking DT. We conclude that the FIWARE Ecosystem constitutes a real reference option for developing DTs in any domain.
Educational escape rooms are taxing in terms of the time needed to design, create, conduct, and evaluate. Therefore, a high "return on investment" is expected regarding their potential to improve ...teaching and learning. Whereas many studies have been conducted to assess the impact of educational escape rooms on learning, results have been so far inconclusive. Several studies have reported positive learning gains, whereas others have not demonstrated such learning gains. To provide a synthesis of the existing empirical body of knowledge, we performed a meta-analysis by pooling the effect sizes across 33 published studies (5,322 observations). Our results suggest that educational escape rooms are highly effective learning activities (Cohen's d = 1.4). The impact on learning was consistent across diverse fields and educational levels, regardless of team size and technology involved. Yet, educational escape rooms conducted remotely yielded smaller learning gains than those conducted face-to-face. Studies comparing educational escape rooms to other educational activities -while scarce- did not suggest a significant superiority of these activities to traditional learning activities, e.g., lectures.
In addition to being a well-liked form of recreation, escape rooms have drawn the attention of educators due to their ability to foster teamwork, leadership, creative thinking, and communication in a ...way that is engaging for students. As a consequence, educational escape rooms are emerging as a new type of learning activity under the promise of enhancing students' learning through highly engaging experiences. These activities consist of escape rooms that incorporate course materials within their puzzles in such a way that students are required to master these materials in order to succeed. Although several studies have reported on the use of escape rooms in a wide range of disciplines, prior research falls short of addressing the use of educational escape rooms for teaching programming, one of the most valuable skills of the twenty-first century that students often have difficulties grasping. This paper reports on the use of an educational escape room in a programming course at a higher education institution and provide, for the first time, insights on the instructional effectiveness of using educational escape rooms for teaching programming. The results of this paper show that appropriate use of educational escape rooms can have significant positive impacts on student engagement and learning in programming courses. These results also suggest that students prefer these activities over traditional computer laboratory sessions. Finally, another novel contribution of this paper is a set of recommendations and proposals for educators in order to help them create effective educational escape rooms for teaching programming.
There is a paucity of longitudinal studies in online learning across courses or throughout programs. Our study intends to add to this emerging body of research by analyzing the longitudinal ...trajectories of interaction between student engagement and achievement over a full four-year program. We use learning analytics and life-course methods to study how achievement and engagement are intertwined and how such relationship evolves over a full program for 106 students. Our findings have indicated that the association between engagement and achievement varies between students and progresses differently between such groups over time. Our results showed that online engagement at any single time-point is not a consistent indicator for high achievement. It takes more than a single point of time to reliably forecast high achievement throughout the program. Longitudinal high grades, or longitudinal high levels of engagement (either separately or combined) were indicators of a stable academic trajectory in which students remained engaged —at least on average— and had a higher level of achievement. On the other hand, disengagement at any time point was consistently associated with lower achievement among low-engaged students. Improving to a higher level of engagement was associated with —at least— acceptable achievement levels and rare dropouts. Lack of improvement or “catching up” may be a more ominous sign that should be proactively addressed.
•The relationship between engagement and achievement is complex, longitudinal, and heterogenous.•Only a subset of students maintained stable and consistent engagement with high academic achievement.•Students who improved after a turbulent start were able to maintain a reasonable level of achievement.•Students who disengaged or failed to improve their engagement were at the highest risk of low achievement.•A method for multi-channel sequence mining is described for studying states, transitions, and trajectories.
This study empirically investigates diffusion-based centralities as depictions of student role-based behavior in information exchange, uptake and argumentation, and as consistent indicators of ...student success in computer-supported collaborative learning. The analysis is based on a large dataset of 69 courses (n = 3,277 students) with 97,173 total interactions (of which 8,818 were manually coded). We examined the relationship between students’ diffusion-based centralities and a coded representation of their interactions in order to investigate the extent to which diffusion-based centralities are able to adequately capture information exchange and uptake processes. We performed a meta-analysis to pool the correlation coefficients between centralities and measures of academic achievement across all courses while considering the sample size of each course. Lastly, from a cluster analysis using students’ diffusion-based centralities aimed at discovering student role-taking within interactions, we investigated the validity of the discovered roles using the coded data. There was a statistically significant positive correlation that ranged from moderate to strong between diffusion-based centralities and the frequency of information sharing and argumentation utterances, confirming that diffusion-based centralities capture important aspects of information exchange and uptake. The results of the meta-analysis showed that diffusion-based centralities had the highest and most consistent combined correlation coefficients with academic achievement as well as the highest predictive intervals, thus demonstrating their advantage over traditional centrality measures. Characterizations of student roles based on diffusion centralities were validated using qualitative methods and were found to meaningfully relate to academic performance. Diffusion-based centralities are feasible to calculate, implement and interpret, while offering a viable solution that can be deployed at any scale to monitor students’ productive discussions and academic success.
The pace of our knowledge on online engagement has not been at par with our need to understand the temporal dynamics of online engagement, the transitions between engagement states, and the factors ...that influence a student being persistently engaged, transitioning to disengagement, or catching up and transitioning to an engaged state. Our study addresses such a gap and investigates how engagement evolves or changes over time, using a person-centered approach to identify for whom the changes happen and when. We take advantage of a novel and innovative multistate Markov model to identify what variables influence such transitions and with what magnitude, i.e., to answer the why. We use a large data set of 1428 enrollments in six courses (238 students). The findings show that online engagement changes differently —across students— and at different magnitudes —according to different instructional variables and previous engagement states. Cognitively engaging instructions helped cognitively engaged students stay engaged while negatively affecting disengaged students. Lectures —a resource that requires less mental energy— helped improve disengaged students. Such differential effects point to the different ways interventions can be applied to different groups, and how different groups may be supported. A balanced, carefully tailored approach is needed to design, intervene, or support students' engagement that takes into account the diversity of engagement states as well as the varied response magnitudes that intervention may incur across diverse students’ profiles.
•Transitions and changes in engagement are largely influenced by instructional variables•Students' subgroups respond differently to instructional variables•Cognitively engaged students persist or improve with cognitively engaging learning resources•Disengaged students fare better with behaviorally engaging resources.•The ability to transition to a favorable state explains performance
Learning programming is a complex and challenging task for many students. It involves both understanding theoretical concepts and acquiring practical skills. Hence, analyzing learners’ data from ...online learning environments alone fails to capture the full breadth of students’ actions if part of their learning process takes place elsewhere. Moreover, existing studies on learning analytics applied to programming education have mainly relied on frequency analysis to classify students according to their approach to programming or to predict academic achievement. However, frequency analysis provides limited insights into the individual time-related characteristics of the learning process. The current study examines students’ strategies when learning programming, combining data from the learning management system and from an automated assessment tool used to support students while solving the programming assignments. The study included the data of 292 engineering students (228 men and 64 women, aged 20–26) from the two aforementioned sources. To gain an in-depth understanding of students’ learning process as well as of the types of learners, we used learning analytics methods that account for the temporal order of learning actions. Our results show that students have special preferences for specific learning resources when learning programming, namely, slides that support search, and copy and paste. We also found that videos are relatively less consumed by students, especially while working on programming assignments. Lastly, students resort to course forums to seek help only when they struggle.
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