From dirty data to multiple versions of truth: How different choices in data cleaning lead to different learning analytics outcomes

Learning analytics is the analysis of student data with the purpose of improving learning. However, the process of data cleaning remains underexposed within learning analytics literature. In this paper, we elaborate on choices made in the cleaning process of student data and their consequences. We illustrate this with a case where data was gathered during six courses taught via Moodle. In this data set, only 21% of the logged activities were linked to a specific course. We illustrate possible choices in dealing with missing data by applying the cleaning process twelve times with different choices on copies of the raw data. Consequently, the analysis of the data shows varying outcomes. As the purpose of learning analytics is to intervene based on analysis and visualizations, it is of utmost importance to be aware of choices made during data cleaning. This paper\u27s main goal is to make stakeholders of (learning) analytics activities aware of the fact that choices are made during data cleaning have consequences on the outcomes. We believe that there should be transparency to the users of these outcomes and give them a detailed report of the decisions made

Unravelling the dynamics of instructional practice: a longitudinal study on learning design and VLE activities

Substantial progress has been made in understanding how teachers design for learning. However, there remains a paucity of evidence of the actual students' response towards leaning designs. Learning analytics has the power to provide just-in-time support, especially when predictive analytics is married with the way teachers have designed their course, or so-called a learning design. This study investigates how learning designs are configured over time and their impact on student activities by analyzing longitudinal data of 38 modules with a total of 43,099 registered students over 30 weeks at the Open University UK, using social network analysis and panel data analysis. Our analysis unpacked dynamic configurations of learning designs between modules over time, which allows teachers to reflect on their practice in order to anticipate problems and make informed interventions. Furthermore, by controlling for the heterogeneity between modules, our results indicated that learning designs were able to explain up to 60% of the variability in student online activities, which reinforced the importance of pedagogical context in learning analytics

FINDING TOPICS IN CREATIVE WRITING ON ENVIRONMENTAL PRESERVATION FOR BETTER TEACHING STRATEGIES: A CASE OF STUDY IN AN ELEMENTARY SCHOOL FROM COLOMBIA

In this research, essays on trees preservation of fourth grade students (elementary school from Colombia) were evaluated with Latent Dirichlet Allocation (LDA). The objective was extracting the fundamental topics, to understand the students’ behavior and awareness towards the environment from the creative writing. The computational results suggest the student’s reflections on environment preservation are focused on five main topics in: Teach-Learn to care for the environment, Explore-discover the environment, Well-being of the environment, Concern for the environment, and Restoration and conservation of the environment. This text analysis by LDA can complement the manual analysis of teachers, avoiding the veracity bias and allowing the enhancement of teaching strategies.En esta investigación, se evaluaron ensayos sobre la preservación de árboles de estudiantes de cuarto grado (escuela primaria de Colombia) con Latent Dirichlet Allocation (LDA). El objetivo fue extraer los temas fundamentales, para comprender el comportamiento y la conciencia de los estudiantes hacia el medio ambiente a partir de la escritura creativa. Los resultados computacionales sugieren que las reflexiones del estudiante sobre la preservación del medio ambiente se centran en cinco temas principales en: Enseñar-Aprender a cuidar el medio ambiente, Explorar-descubrir el medio ambiente, Bienestar del medio ambiente, Preocupación por el medio ambiente y Restauración y conservación del entorno. Este análisis de texto por LDA puede complementar el análisis manual de los docentes, evitando el sesgo de veracidad y permitiendo potenciar las estrategias de enseñanza

A LAK of Direction Misalignment Between the Goals of Learning Analytics and its Research Scholarship

Learning analytics defines itself with a focus on data from learners and learning environments, with corresponding goals of understanding and optimizing student learning. In this regard, learning analytics research, ideally, should be characterized by studies that make use of data from learners engaged in education systems, should measure student learning, and should make efforts to intervene and improve these learning environments

Machine and expert judgments of student perceptions of teaching behavior in secondary education:Added value of topic modeling with big data

Research shows that effective teaching behavior is important for students' learning and outcomes, and scholars have developed various instruments for measuring effective teaching behavior domains. Although student assessments are frequently used for evaluating teaching behavior, they are mainly in Likert-scale or categorical forms, which precludes students from freely expressing their perceptions of teaching. Drawing on an open-ended questionnaire from large-scale student surveys, this study uses a machine learning tool aiming to extract teaching behavior topics from large-scale students’ open-ended answers and to test the convergent validity of the outcomes by comparing them with theory-driven manual coding outcomes based on expert judgments. We applied a latent Dirichlet allocation (LDA) topic modeling analysis, together with a visualization tool (LDAvis), to qualitative data collected from 173,858 secondary education students in the Netherlands. This data-driven machine learning analysis yielded eight topics of teaching behavior domains: Clear explanation, Student-centered supportive learning climate, Lesson variety, Likable characteristics of the teacher, Evoking interest, Monitoring understanding, Inclusiveness and equity, Lesson objectives and formative assessment. In addition, we subjected 864 randomly selected student responses from the same dataset to manual coding, and performed theory-driven content analysis, which resulted in nine teaching behavior domains and 19 sub-domains. Results suggest that the relation between machine learning and human analysis is complementary. By comparing the bottom-up (machine learning analysis) and top-down (content analysis), we found that the proposed topic modeling approach reveals unique domains of teaching behavior, and confirmed the validity of the topic modeling outcomes evident from the overlapping topics

Blended Learning and Teaching in Higher Education: An International Perspective

Blended learning is not a new topic for educational research in Higher Education (HE). However, before the first wave of the Covid-19 pandemic, blended learning was studied by a "niche" of researchers and educators interested in technology integration in teaching and learning. It was not difficult to meet HE professionals who had never or only poorly reflected on the topic of how to integrate digital technology in teaching and learning before March 2020. All in all, this special issue provides a deeper understanding of what Blended Learning will be in the near feature, encompassing not the simple combination of online and physical presence, but a combination of delivery tools and media used to provide information and to support interaction, a combination of different methods of instruction and teaching/learning, and a combination of learning contexts

Video-based collaborative learning:evidence for a pedagogical model

The educational potential of video is a long-lasting, multi-faceted topic, and the affordances of technological advancement have recently revitalized this discussion. However, teachers are still far from competently integrating or becoming accustomed to video-based pedagogy, especially in combination with collaborative pedagogy. To provide teachers and teacher educators with sound principles for implementing video-supported collaborative learning (VSCL), this symposium fosters a teacher experiment, a cross-over analysis on a pedagogical model for effective VSCL, and student feedback in relation with VSCL. The experiment shows students’ growing lexical richness and cohesion by working peer feedback on student’s video recorded teaching practice. The cross-over analysis shows the evidence for the VSCL-pedagogical model based on data from many other experiments in the European ViSuAL-project. The same holds for the student-feedback analysis. In this symposium we interact about practical experiences in relation with the effective principles of the developed pedagogical model and the experiences of the students