# Insights from Learning Analytics for Hands-On Cloud Computing Labs in AWS
## 1 Introduction  
The beginning of the digital era has exponentially increased the amount of data generated. The new data analytic techniques have received special attention in the research from the industrial and academic sectors [1]. The data sources that have emerged in the past years have allowed better characterization of student behavior. The use of learning management systems (LMSs) has increased in recent years, especially in universities that offer online courses where students can immerse themselves in an individual and collaborative learning experience. In higher education, the use of analytics is an active area of research. Indeed, there are many different concepts when it comes to analytics, and finding a definition that fits all profiles can be complicated [2]. In the work by [PERSON] et al. [3], several definitions are collected according to different terms, and they proposed the following: \"an overarching concept described as data-driven decision making\". The growing interest in improving the students' learning methods has led to the creation of different institutions specialized in exploring the role and impact of analytics in teaching and learning in the education sector. In 2011, the Society for Learning Analytics Research (SoLAR: [[http://www.solaresearch.org](http://www.solaresearch.org)]([http://www.solaresearch.org](http://www.solaresearch.org))) was founded as a non-profit interdisciplinary network of international researchers to explore the impact of big data and learning analytics in the education sector. Learning analytics in academia focuses on gathering the data generated by students during courses to manage student success, including early warning processes where the need for intervention by a teacher can be justified. With the growth of interest in research on learning analytics, the large amount of data generated in the education sector, and the rapid development of applications and software for data collection, it is important that researchers and educators recognize the characteristics of educational data mining (EDM) and learning analytics and knowledge (LAK). These data-intensive education approaches are increasingly prominent in sectors such as government, health care, and industry [4]. Indeed, as described in [5], the analysis of the data generated by students from the use of online technologies can provide information on the students' progress and the quality of the teaching and curriculum implemented. The analysis of student follow-up data has led to the emergence of new technologies around adaptive learning and recommendation systems for adaptive practice. In [6], [PERSON] defines multimodal learning analytics (MMLA) as \"a set of techniques that can be used to collect multiple sources of data in high frequency (video, logs, audio, gestures, biosensors), synchronize and code the data, and examine learning in realistic, ecologically valid, social, mixed-media learning environments.\" In this line, the work by [PERSON] [7] indicates that MMLA \"utilizes and triangulates among non-traditional as well as traditional forms of data in order to characterize or model student learning in complex learning environments\". We believe that cloud computing instruction can benefit from the automated compilation of learning analytics, which can then be coupled to additional analytics obtained from LMSs to provide further insights. With this aim, this paper focuses on capturing and analyzing learning analytics gathered from a fee-paying online course in cloud computing with Amazon Web Services (AWS) in order to reveal data-driven insights concerning the students. These data include: (i) activity logs from their interaction with multiple cloud services to determine the students' percentage of progress for each hands-on session and (ii) results of the self-assessment tests that students can optionally take after each module.