Technologies for Data-Driven Interventions in Smart Learning Environments [Editorial]

Smart Learning environments (SLEs) are defined [1] as learning ecologies where students engage in learning activities, or where teachers facilitate such activities with the support of tools and technology. SLEs can encompass physical or virtual spaces in which a system senses the learning context and process by collecting data, analyzes the data, and consequently reacts with customized interventions that aim at improving learning [1]. In this way, SLEs may collect data about learners and educators’ actions and interactions related to their participation in learning activities as well as about different aspects of the formal or informal context in which they can be carried out. Sources from these data may include learning management systems, handheld devices, computers, cameras, microphones, wearables, and environmental sensors. These data can then be transformed and analyzed using different computational and visualization techniques to obtain actionable information that can trigger a wide range of automatic, human-mediated, or hybrid interventions, which involve learners and teachers in the decision making behind the interventions.This work was supported in part by the Spanish Ministry of Science and Innovation through Smartlet and the H2OLearn Projects under Grant MICIN/AEI/10.13039/501100011033, and in part by the Fondo Europeo de Desarrollo Regional (FEDER) under Grant TIN2017-85179-C3-1-R, Grant TIN2017-85179-C3-2-R, Grant TIN2017-85179-C3-30R, Grant PID2020-112584RB-C31, Grant PID2020-112584RB C32, and Grant GPID2020-112584RB-C33. The work of Davinia Hernández-Leo (Serra Húnter) was supported by ICREA through the ICREA Academia Program.Publicad

Analyzing CMC content for what?

Computer mediated communication (CMC) refers to communication between individuals and among groups via networked computers. Such forms of communication can be asynchronous or synchronous and serve a wide variety of useful functions ranging from administration to building understanding and knowledge. As such there are many reasons for interest in analyzing the content of CMC. Foremost among these is the opportunity that the written text is able to offer for various types of analysis. Others have to do with the need to understand human communication patterns in this medium, their conventions, form and functions, the nature of the subtext within it, and how people derive meaning and understanding in such contexts. The papers in this special section of this journal have attempted to closely examine the subject of CMC content analysis. It includes examination of what is involved in the analysis of CMC content, schemes and frameworks for analyzing them, and knowledge building within such contexts

Revealing the hidden structure of physiological states during metacognitive monitoring in collaborative learning

Using hidden Markov models (HMM), the current study looked at how learners' metacognitive monitoring is related to their physiological reactivity in the context of collaborative learning. The participants (N = 12, age 16-17 years, three females and nine males) in the study were high school students enrolled in an advanced physics course. The results show that during collaborative learning, the students engaged in monitoring in each self-regulated learning phase such as task understanding, planning and goal setting, task enactment, adaptation and reflection. The results of the HMM indicated that the learners' physiological reactivity was low when monitoring occurred. The associations between the states based on the HMM provide insights not only into how learners engage in metacognitive monitoring but also about their level of physiological reactivity in each state. In conclusion, exploring aspects of metacognitive monitoring in collaborative learning can be done with the help of physiological reactions