Evidence of the ISTE Standards for Educators Leading to Learning Gains

The International Society for Technology in Education (ISTE) empirically designed and published educator standards to provide a roadmap for educators on effective technology integration. The purpose of this further study was to determine what empirical evidence demonstrates that the educator practices have a positive impact on student learning. Using a scoping review methodology, a transparent protocol was used for searching, identifying, and selecting articles that map to the practices within the ISTE Standards. The findings of this study reveal that all the practices in ISTE educator standards led to learning gains. This study is important for researchers, practitioners, funders, and policymakers as it provides empirical evidence that the technology practices within the ISTE Standards lead to student learning gains

Learning analytics to assess students’ behavior with scratch through clickstream

The construction of knowledge through computational practice requires to teachers a substantial amount of time and effort to evaluate programming skills, to understand and to glimpse the evolution of the students and finally to state a quantitative judgment in learning assessment. This suposes a huge problem of time and no adecuate intime feedback to students while practicing programming activities. The field of learning analytics has been a common practice in research since last years due their great possibilities in terms of learning improvement. Such possibilities can be a strong positive contribution in the field of computational practice such as programming. In this work we attempt to use learning analytics to ensure intime and quality feedback through the analysis of students behavior in programming practice. Hence, in order to help teachers in their assessments we propose a solution to categorize and understand students’ behavior in programming activities using business technics such as web clickstream. Clickstream is a technique that consists in the collection and analysis of data generated by users. We applied it in learning programming environments to study students behavior to enhance students learning and programming skills. The results of the work supports this business technique as useful and adequate in programming practice. The main finding showns a first taxonomy of programming behaviors that can easily be used in a classroom. This will help teachers to understand how students behave in their practice and consequently enhance assessment and students’ following-up to avoid examination failures.Peer ReviewedPostprint (published version

Learning Analytics for the Formative Assessment of New Media Skills

Recent theories of education have shifted learning environments towards student-centred education. Also, the advancement of technology and the need for skilled individuals in different areas have led to the introduction of new media skills. Along with new pedagogies and content, these changes require new forms of assessment. However, assessment as the core of learning has not been modified as much as other educational aspects. Hence, much attention is required to develop assessment methods based on current educational requirements. To address this gap, we have implemented two data-driven systematic literature reviews to recognize the existing state of the field in the current literature. Chapter four of this thesis focus on a literature review of automatic assessment, named learning analytics. This chapter investigates the topics and challenges in developing new learning analytics tools. Chapter five studies all assessment types, including traditional and automatic forms, in computational thinking education. Computational thinking education, which refers to the teaching of problem-solving skills, is one of the new media skills introduced in the 21st century. The findings from these two literature reviews categorize the assessment methods and identify the key topics in the literature of learning analytics and computational thinking assessment. Studying the identified topics, their relations, and related studies, we pinpoint the challenges, requirements, and opportunities of using automatic assessment in education. The findings from these studies can be used as a guideline for future studies aiming to enhance assessment methods in education. Also, the literature review strategy in this thesis can be utilized by other researchers to develop systematic data-driven literature reviews in future studies

Clickstream for learning analytics to assess students’ behavior with Scratch

The construction of knowledge through computational practice requires to teachers a substantial amount of time and effort to evaluate programming skills, to understand and to glimpse the evolution of the students and finally to state a quantitative judgment in learning assessment. The field of learning analytics has been a common practice in research since last years due to their great possibilities in terms of learning improvement. Both, Big and Small data techniques support the analysis cycle of learning analytics and risk of students’ failure prediction. Such possibilities can be a strong positive contribution to the field of computational practice such as programming. Our main objective was to help teachers in their assessments through to make those possibilities effective. Thus, we have developed a functional solution to categorize and understand students’ behavior in programming activities based in Scratch. Through collection and analysis of data generated by students’ clicks in Scratch, we proceed to execute both exploratory and predictive analytics to detect patterns in students’ behavior when developing solutions for assignments. We concluded that resultant taxonomy could help teachers to better support their students by giving real-time quality feedback and act before students deliver incorrectly or at least incomplete tasks.Peer ReviewedPostprint (author's final draft

Computing Competencies for Undergraduate Data Science Curricula: ACM Data Science Task Force

At the August 2017 ACM Education Council meeting, a task force was formed to explore a process to add to the broad, interdisciplinary conversation on data science, with an articulation of the role of computing discipline-specific contributions to this emerging field. Specifically, the task force would seek to define what the computing/computational contributions are to this new field, and provide guidance on computing-specific competencies in data science for departments offering such programs of study at the undergraduate level. There are many stakeholders in the discussion of data science – these include colleges and universities that (hope to) offer data science programs, employers who hope to hire a workforce with knowledge and experience in data science, as well as individuals and professional societies representing the fields of computing, statistics, machine learning, computational biology, computational social sciences, digital humanities, and others. There is a shared desire to form a broad interdisciplinary definition of data science and to develop curriculum guidance for degree programs in data science. This volume builds upon the important work of other groups who have published guidelines for data science education. There is a need to acknowledge the definition and description of the individual contributions to this interdisciplinary field. For instance, those interested in the business context for these concepts generally use the term “analytics”; in some cases, the abbreviation DSA appears, meaning Data Science and Analytics. This volume is the third draft articulation of computing-focused competencies for data science. It recognizes the inherent interdisciplinarity of data science and situates computing-specific competencies within the broader interdisciplinary space

Responsible AI and Analytics for an Ethical and Inclusive Digitized Society

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