The human gaze is a promising input modality for interactive applications due to its advantages: giving benefits to motion-impaired people while providing faster, intuitive, and easy interaction. The most common form of gaze interaction is object selection. During the last decade, gaze gestures and smooth pursuit-based interaction have been emerging techniques for spontaneous object selection in various gaze-controlled applications. Unfortunately, the challenge of spontaneous interaction demands no prior gaze-to-screen calibration, which leads to inaccurate object selection. To overcome the accuracy issue, this article proposes a novel method for spontaneous gaze interaction based on Pearson product-moment correlation as a measure of similarity, an exponential moving average filter for signal denoising, and a hidden Markov model to perform eye movement classification. Based on experimental results, our approach yielded the best object selection accuracy and success time of$\text{89.60}\pm \text{10.59}\%$and$\text{4364}\pm \text{235.86}$ms, respectively. Our results imply that spontaneous interaction for gaze-controlled applications is possible with careful consideration of the underlying techniques to handle noisy data generated by the eye tracker. Furthermore, the proposed method is promising for future development of interactive touchless display systems that comply with the health protocols of the World Health Organization during the COVID-19 pandemic.

Incapability of relational database in handling large-scale data triggers the development of NoSQL database that becomes part of a big data ecosystem. NoSQL database has different characteristics compared to the relational database. However, NoSQL database requires data from the relational database as one of the structured data sources. Therefore, data pre-processing is required to ensure proper data migration from a relational database to NoSQL database. This data pre-processing is normally called data transformation. One of the simple and understandable transformation algorithms is graph transforming algorithm. However, the algorithm has a problem in solving a non-simple graph (multigraph). This research proposes an algorithm to overcome several multigraph problems. The experimental work confirms that the algorithm proposed in this research is able to transform data from a relational database to NoSQL schema that has a minimum number of redundant attributes while the data completeness is still maintained.

Adaptive metacognitive scaffolding is developed to provide learning assistance on an as‐needed basis; thus, advances the effectiveness of computer‐based learning systems. Metacognitive scaffoldings have been developed for some science subjects; however, not for algorithm‐learning. The learning algorithm is different from learning science as it is more oriented to problem‐solving; therefore, this study is aimed to describe the modelling, development, and evaluation of the adaptive metacognitive scaffolding which is dedicated for encouraging algorithm‐learning. In addition, the authors present a new approach for learner modelling to find students’ metacognitive state. Adaptivity of the scaffolding is based on the learner modelling. To evaluate the effectiveness of the developed system, it is deployed in a real algorithm‐learning classroom of 38 students. The class is randomly divided into two groups: experiment and control. Two parameters are measured from both groups, i.e. academic success and academic satisfaction. Non‐parametric statistical test, i.e. Mann–Whitney U‐test (significance level 0.01) rejects the null hypothesis (U‐value = 86.5 and U‐critical = 101). This result verifies that the academic success of the experiment group is significantly higher than that of the control group. In addition, an academic satisfaction survey shows that adaptive scaffolding is valid in assisting students while learning with the system.

There has been awareness of the importance of metacognitive skill in learning processes, especially for university students, who are required to be more self-regulated. Therefore, monitoring the development of such skill is needed to ensure student's achievements. Currently, in classroom learning environments, student's metacognition can be observed conventionally by using interview or think-aloud procedures, however, the tasks are tedious and impractical for big number of students. Therefore, an automatic student's metacognition assessment/modeling is in progress. This paper proposes a partly automatic metacognition assessment, which is an implementation of unsupervised learning method. This proposed method is proven using a case study in which the dataset was gathered by administering Metacognitive Awareness Inventory (MAI) questionnaire to undergraduate students in our department. Experiment shows that the proposed method could be utilized for automatic assessment of student's metacognition. Two groups of students are identified, one that does well in their metacognitive awareness and the other one that needs further guidance and advisory to help them achieve better results and avoid failures.