udacity combining predictive techniques task3

Udacity combining predictive techniques task3 how to#

Higher educational institutions capture huge amounts of educational data, especially in online learning.

By leveraging the novel visualization techniques used in the web-based research study, while staying true to the analytical methods of research design, graduate students, educational researchers, and practitioners will gain a broader understanding of big data and analytics for data use and representation in the field of education. Graduate students, educational researchers, and practitioners can contribute to take strengths from each visual analytical method to enhance the reach of significant research findings into the public sphere. This chapter also presents the potentials of each visualization method for assisting researchers in choosing the most appropriate one in the web-based research study. This chapter presents a general and practical guideline that is intended to introduce the traditional visualization methods (word clouds), and the advanced visualization methods including interactive visualization (heatmap matrix) and dynamic visualization (dashboard), which can be applied in quantitative, qualitative, and mixed-methods research. We conclude that the collected data are perceived as useful by teachers to improve the teaching and learning process. Furthermore, we have processed and presented the data visually to teachers for those scenarios and have collected and analysed their perception of their usefulness. We have deployed it in a real scenario and collected real data. We have developed a solution to collect clickstream analytics data applicable to smaller scenarios, much more common in primary, secondary and higher education, where videos are watched tens or hundreds of times, and to analyse whether the solution is useful to teachers to improve the learning process. Most of the literature focuses on large scale learning scenarios, such as MOOCs, where videos are watched hundreds or thousands of times. There is also some literature on the uses of such data in order to better understand and improve the teaching-learning process. We evaluate the proposed approach on the dataset from a real-world interactive question pool using four typical machine learning models.Ī variety of tools are available to collect, process and analyse learning data obtained from the clickstream generated by students watching learning resources in video format.

In addition, heterogeneous information network is applied to integrating students' historical problem-solving information on similar questions, enhancing student performance predictions on a new question. Specifically, we introduce new features (e.g., think time, first attempt, and first drag-and-drop) based on student mouse movement trajectories to delineate students' problem-solving details. In this paper, we propose a novel approach to boost student performance prediction in interactive online question pools by further considering student interaction features and the similarity between questions.

Udacity combining predictive techniques task3 how to#

It remains unclear how to conduct student performance prediction in interactive online question pools without such well-organized question orders or knowledge tags by experts. However, most existing work on student performance prediction targets at online learning platforms with a well-structured curriculum, predefined question order and accurate knowledge tags provided by domain experts. Interactive online question pools (e.g., educational game platforms), an important component of online education, have become increasingly popular in recent years. Modeling student learning and further predicting the performance is a well-established task in online learning and is crucial to personalized education by recommending different learning resources to different students based on their needs.