Detecting Impasse During Collaborative Problem Solving with Multimodal Learning Analytics

Computational Learning Analytics to Estimate Location-Based Self-Regulation Process of Real-World Experiences

“…Conventional studies [74] have investigated time-series strategy executions changed by self-judging the strategies' effects. However, as discussed in Section I-B, learning analytics has difficulties to automatically estimate a high-level learning situation [12], [13], such as the self-regulation state of learning [16]. In fact, conventional studies have not automatically sensed how a learner with grounded cognition internally selfregulates experience-based symbolic operations in the spatial context of the world.…”

“…An unsolved issue in learning analytics is to capture highlevel semantics of learning [12], [13]. In real-world learning, a learner constructs an internal cognitive representation of knowledge by not only human-world interaction in the context of the real world but also human-human interaction to collaboratively share learners' states of grounded cognition.…”

“…Multimodal learning analytics has been implemented using various data, such as behavior data (e.g., eye movement, speech, face expression), physiological data (e.g., electroencephalogram), and environmental data (e.g., humidity, CO2 concentration) [11]. However, it is claimed that existing studies on multimodal learning analytics have shown the potential to accurately predict simple educational events/behaviors but still struggle to capture complex learning situations (e.g., collaboration quality, the state of acquiring insufficient ideas to progress) [12], [13].…”

“…(1) abstract knowledge, (2) abstraction level of talk, (3) attribute of target, (4) commonality, (5) concrete evidence, (6) concrete knowledge, (7) conflict between other's and group's goals, (8) conflict between own and group's goals, (9) conflict between own and other's goals, (10) current achievement level of learning, (11) current understanding, (12) difference between own and other's ideas, (13) (e.g., "infer", "explain", "assess" [59], [60], [61]), but our target codes were extended to express targets of human-world and human-human interactions, e.g., real-world information, a learner's individual cognition, and others' cognition [4]. Furthermore, the present study additionally observed 30 learners' behavior to update our previous code list with supplementary codes to express grounded cognition, for example, grounding real-world symbols (e.g., O24: ground) and operating the strength of personal belief (e.g., O30: intensify, O54: weaken).…”

LAK of Direction

“…A study that examined different methods for assessing middle school students' use of evidence and systems thinking was coded as measuring learning (Ahn et al, 2021). Two studies that examined children's dialogue during collaborative problem solving were both coded as measuring learning (Emara et al, 2021;Ma et al, 2022). For readers who are skeptical of our findings, rather than presume that we've mischaracterized a learning-oriented literature, we recommend reviewing the article list, and our associated codes, and drawing your own conclusions from the data, which we've made public at https://osf.io/tzhva/.…”