The use of multimedia learning is increasing in modern education. On the other hand, it is crucial to design multimedia contents that impose an optimal amount of cognitive load, which leads to efficient learning. Objective assessment of instantaneous cognitive load plays a critical role in educational design quality evaluation. Electroencephalography (EEG) has been considered a potential candidate for cognitive load assessment among neurophysiological methods. In this study, we experiment to collect EEG signals during a multimedia learning task and then build a model for instantaneous cognitive load measurement. In the experiment, we designed four educational multimedia in two categories to impose different levels of cognitive load by intentionally applying/violating Mayer’s multimedia design principles. Thirty university students with homogenous English language proficiency participated in our experiment. We divided them randomly into two groups, and each watched a version of the multimedia followed by a recall test task and filling out a NASA-TLX questionnaire. EEG signals are collected during these tasks. To construct the load assessment model, at first, power spectral density (PSD) based features are extracted from EEG signals. Using the minimum redundancy - maximum relevance (MRMR) feature selection approach, the best features are selected. In this way, the selected features consist of only about 12% of the total number of features. In the next step, we propose a scoring model using a support vector machine (SVM) for instantaneous cognitive load assessment in 3s segments of multimedia. Our experiments indicate that the selected feature set can classify the instantaneous cognitive load with an accuracy of 84.5 ± 2.1%. The findings of this study indicate that EEG signals can be used as an appropriate tool for measuring the cognitive load introduced by educational videos. This can be help instructional designers to develop more effective content.

Temporal synchronization of behavioral and physiological signals collected through different devices (and sometimes through different computers) is a longstanding challenge in HCI, neuroscience, psychology, and related areas. Previous research has proposed to synchronize sensory signals using (1) dedicated hardware; (2) dedicated software; or (3) alignment algorithms. All these approaches are either vendor-locked, non-generalizable, or difficult to adopt in practice. We propose a simple but highly efficient alternative: instrument the stimulus presentation software by injecting supervisory event-related timestamps, followed by a post-processing step over the recorded log files. Armed with this information, we introduce Gustav, our approach to orchestrate the recording of sensory signals across devices and computers. Gustav ensures that all signals coincide exactly with the duration of each experiment condition, with millisecond precision. Gustav is publicly available as open source software. CCS CONCEPTS• Human-centered computing → Laboratory experiments; Interaction devices; HCI design and evaluation methods.

The interaction context (or environment) is key to any HCI task and especially to adaptive user interfaces (AUIs), since it represents the conditions under which users interact with computers. Unfortunately, there are currently no formal representations to model said interaction context. In order to address this gap, we propose a contextual framework for AUIs and illustrate a practical application using learning management systems as a case study. We also discuss limitations of our framework and offer discussion points about the realisation of truly context-aware AUIs. CCS CONCEPTS• Human-centered computing → Interactive systems and tools; HCI theory, concepts and models; Ubiquitous and mobile computing theory, concepts and paradigms.