Laboratory activities are playing a substantial role in supporting scientific learning fields by enabling students to obtain practical skills through experiments and by giving them the chance to have a more profound understanding of the content. Although laboratory activities are expensive and time-consuming, virtual laboratory activities can save money, time, and effort. The Virtual Science Lab (VSL) is a web-based platform designed to improve learning approaches by introducing a safe and interactive lab environment for students in middle schools in Saudi Arabia. VSL was found to be an exciting, useful, and enjoyable learning environment during user trials. It allowed users to conduct experiments individually and to repeat them multiple times if needed.

<p>Data stream is the huge amount of data generated in various fields, including financial processes, social media activities, Internet of Things applications, and many others. Such data cannot be processed through traditional data mining algorithms due to several constraints, including limited memory, data speed, and dynamic environment. Concept Drift is known as the main constraint of data stream mining, mainly in the classification task. It refers to the change in the data stream underlining distribution over time. Thus, it results in accuracy deterioration of classification models and wrong predictions. Spam emails, consumer behavior changes, and adversary activates, are examples of Concept Drift. In this paper, a Concept Drift detection model is introduced, Concept Drift Detection Model (CDDM). It monitors the accuracy of the classification model over a sliding window, assuming the decline in accuracy indicates a drift occurrence. A modification over CDDM is a weighted version of the CDDM as W-CDDM.</p><p>Both models have evaluated against two real datasets and four artificial datasets. The experimental results of abrupt drift show that CDDM, W-CDDM outperforms the other models in the dataset of 100K and 1M instances, respectively. Regarding gradual drift, the W-CDDM overtook the rest in terms of accuracy, run time, and detection delays in the dataset of 100 K instances. While in the dataset of 1M instances, CDDM has got the highest accuracy using the NB classifier. Moreover, W-CDDM achieves the highest accuracy on real datasets.</p>

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