PERBANDINGAN TINGKAT USABILITY GOOGLE CLASSROOM BERDASARKAN PERSPEKTIF TEACHERS PADA PERGURUAN TINGGI

E-Learning merupakan aplikasi teknologi informasi yang berbasis elektronik melalui jaringan internet yang dirancang untuk keperluan pembelajaran. Saat ini hampir semua sekolah tinggi telah memanfaatkan e-Learning dalam proses pembelajaran. Google merupakan perusahaan besar yang menawarkan fasilitas Google for Education, dengan salah satu produknya adalah google classroom. Google classroom dapat membantu students dan teachers untuk mengorganisasi penugasan, mendukung kolaborasi, dan membantu komunikasi yang lebih baik. Ketersediaan google classroom pada sisi students dan teachers belum sepenuhnya membuat aplikasi digunakan. Penerimaan students dianggap tidak memiliki pengaruh yang signifikan. Sementara itu, performa memiliki pengaruh terhadap tingkat penerimaan. Penelitian ini bertujuan untuk mengukur tingkat usability penggunaan google classroom di lingkungan Perguruan Tinggi melalui perspektif teacher pada Universitas XYZ. Penggunaan google classroom diimplementasikan melalui website desktop dan aplikasi mobile. Hasil pengukuran menunjukkan bahwa google classroom yang diakses melalui website desktop mendapatkan nilai 86, yang berarti acceptable, adjective range dinilai excellent, grade scale berada pada level B. Sementara yang diakses melalui aplikasi mobile mendapatkan nilai 76, dengan penerimaan acceptable, nilai adjective range adalah good, dan grade score pada level C.Kata Kunci: e-Learning, google classroom, usability

Continuous Stress Monitoring under Varied Demands Using Unobtrusive Devices

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This research aims to identify a feasible model to predict a learner’s stress in an online learning platform. It is desirable to produce a cost-effective, unobtrusive and objective method to measure a learner’s emotions. The few signals produced by mouse and keyboard could enable such solution to measure real world individual’s affective states. It is also important to ensure that the measurement can be applied regardless the type of task carried out by the user. This preliminary research proposes a stress classification method using mouse and keystroke dynamics to classify the stress levels of 190 university students when performing three different e-learning activities. The results show that the stress measurement based on mouse and keystroke dynamics is consistent with the stress measurement according to the changes of duration spent between two consecutive questions. The feedforward back-propagation neural network achieves the best performance in the classification

The Effects of Menu Design on Users’ Emotions, Search Performance and Mouse Behaviour

One should not ignore the fact that affect (or emotion) plays an important role in cognition and learning. For instance, badly designed interface brings negative impact on user’s performance if the user does not find enjoyment in his or her overall experience with the system. Automatic analyses of user behaviour in adaptive e-learning system development is important and it would be good to have an effective yet flexible computation metrics to learn user’s emotion, so that necessary adaptation could be provided to enhance user experience. The introduction of keyboard and mouse analyses shed a light to the development of a non-intrusive and inexpensive automated emotion detection method, as these peripherals are part of the computer system. This research investigates the effects of menu design on users’ emotion, search task performance and their mouse behaviours. The results show that the effects of menu design on users’ search task performance and their mouse behaviours are statistically significant. Menu design factors do affect users’ emotions, which they feel uncomfortable with bad combination of colours, smaller font size, text without code, abbreviated text, use of ambiguous term, random display and the need to scroll. However, their discomfort with the bad menu design does not necessarily affect their search job performance

Detecting and Modelling Stress Levels in E-Learning Environment Users

A modern Intelligent Tutoring System (ITS) should be sentient of a learner's cognitive and affective states, as a learner’s performance could be affected by motivational and emotional factors. It is important to design a method that supports low-cost, task-independent and unobtrusive sensing of a learner’s cognitive and affective states, to improve a learner's experience in e-learning, as well as to enable personalized learning. Although tremendous related affective computing research were done in this area, there is a lack of empirical research that can automatically measure a learner's stress using objective methods. This research is set to examine how an objective stress measurement model can be developed, to compute a learner’s cognitive and emotional stress automatically using mouse and keystroke dynamics. To ensure the measurement is not affected even if the user switches between tasks, three preliminary research experiments were carried out based on three common tasks during e-learning − search, assessment and typing. A stress measurement model was then built using the datasets collected from the experiments. Three stress classifiers were tested, namely certainty factors, feedforward back-propagation neural network and adaptive neuro-fuzzy inference system. The best classifier was then integrated into the ITS stress inference engine, which is designed to decide necessary adaptation, and to provide analytical information of learners' performances, which include stress levels and learners’ behaviours when answering questions