Indiana University-Purdue University Indianapolis (IUPUI) / This research took place as a cognitive exploration of sensemaking of learning analytics at Ivy Tech Community College of Indiana. For the courses with the largest online enrollment, quality standards in the course design are maintained by creating sections from a course design framework. This means all sections have the same starting content and the same framework for assessment. The course design framework is maintained by the curriculum committee composed of program chairs who oversee the program to which the course belongs. This research proposed to develop a learning analytics dashboard to elicit the best practices in instantiating a course design framework from the perspective of the program chair. The Instructional Design Implementation Dashboard, IDID, was designed to address the sensemaking needs of program chairs. The program chairs were asked to make sense of IDID built around the data collected from the course management system and the student information system. IDID leveraged metrics from the user activity and the learner performance from the learning management system, combined with data about the student demographics captured from the student information system. IDID was used to identify highly successful sections and examine the instructor behaviors that might be considered best practices. Data Frame Sensemaking theory was confirmed as an accurate description of the experience of program chairs when using IDID. A revised model of Data Frame Sensemaking theory was developed to explain the interaction of those using the IDID platform.

Índice del video: Daniel Navarrete (00:19 - 23:50); Daniela Sarzosa (24:00 - 47:12); Jorge Maldonado (48:30-1:13:30); Jimmy Pérez (1:13:30 - 1:13:55) / Primera meetup del 2019 de la comunidad Learning Analytics Perú, donde se conversó sobre experiencias de éxito en el uso de herramientas, estrategias y métodos que han conducido a resultados satisfactorios en la aplicación estratégica de los datos en Educación Superior, de la mano de profesionales de Perú, Colombia y Ecuador... Evento realizado el 9 de enero de 2019 en la Universidad Peruana de Ciencias Aplicadas, campus San Isidro, auditorio Luis Bustamante.

Accede a la filmación de la conferencia en : http://hdl.handle.net/10757/624838 / Presentación realizada en el marco de la primera meetup del 2019 de la comunidad Learning Analytics Perú, donde se conversó sobre experiencias de éxito en el uso de herramientas, estrategias y métodos que han conducido a resultados satisfactorios en la aplicación estratégica de los datos en Educación Superior, de la mano de profesionales de Perú, Colombia y Ecuador.

Accede a la filmación de la conferencia en : http://hdl.handle.net/10757/624838 / Presentación realizada en el marco de la primera meetup del 2019 de la comunidad Learning Analytics Perú, donde se conversó sobre experiencias de éxito en el uso de herramientas, estrategias y métodos que han conducido a resultados satisfactorios en la aplicación estratégica de los datos en Educación Superior, de la mano de profesionales de Perú, Colombia y Ecuador.

Parte de la conferencia: Experiencias exitosas en el uso de learning analytics en educación superior. Accede a la filmación de la conferencia en : http://hdl.handle.net/10757/624838 / Presentación realizada en el marco de la primera meetup del 2019 de la comunidad Learning Analytics Perú, donde se conversó sobre experiencias de éxito en el uso de herramientas, estrategias y métodos que han conducido a resultados satisfactorios en la aplicación estratégica de los datos en Educación Superior, de la mano de profesionales de Perú, Colombia y Ecuador.

Parte de la conferencia: Experiencias exitosas en el uso de learning analytics en educación superior. Accede a la filmación de la conferencia en : http://hdl.handle.net/10757/624838 / Presentación realizada en el marco de la primera meetup del 2019 de la comunidad Learning Analytics Perú, donde se conversó sobre experiencias de éxito en el uso de herramientas, estrategias y métodos que han conducido a resultados satisfactorios en la aplicación estratégica de los datos en Educación Superior, de la mano de profesionales de Perú, Colombia y Ecuador.

With the continued digitization of higher education, students’ ability to selfregulate their studies in online and blended learning settings has become critical for their academic success. Goal-setting strategies are an important aspect of self-regulated learning which universities aim to support through the implementation of the intended learning outcomes (ILOs) of courses and programs. These act as a promise for students of the knowledge and skills which they are expected to acquire. However, students often perceive an absence of clear connection between ILOs and course assignments that creates a disconnect between students’ course progression and their progression toward course ILOs. To assist students in this task, a student-facing learning analytics dashboard (LAD) allowing students to track and plan their learning progress toward the achievement of the selected course’s ILOs has been developed and evaluated in the context of STEM higher education. The LAD was developed using a participatory design approach combined with design science research methodology. Thirty-seven students contributed to the design of the dashboard through a F2F workshop and later a student feedback session in Spring 2023. The tool was evaluated through five semi-structured interviews informed by the Technology Acceptance Model. The results show students having a behavioral intention to use the dashboard in their everyday university studies. The thesis contributes with a LAD focused on student ILO achievement and task-interest. / Med den fortsatta digitaliseringen av utbildning på högre nivå så har studenters förmåga att självreglera sina studier i online och flerformsundervisning blivit kritisk för deras akademiska framgång. Målsättningstrategier är en viktig aspekt av självreglerad inlärning, universitet siktar på att ge support till studenters målsättningsstrategier genom implementering av kursmål. Dessa agerar som ett löfte till studenter om de kunskaper och förmågor de förväntas uppnått när en kurs är klar. Studenter upplever däremot en brist på tydliga kopplingar mellan kursprogression och progression mot kursmålen. För att hjälpa förtydliga dessa kopplingar till studenter, så har en student riktad learning analytic dashboard (LAD), som tillåter studenter att följa och planera sin progression mot kursmålen, utvecklats och utvärderats i kontexten av STEM högre utbildning. LAD:n utvecklades med användning av en deltagande designstrategi kombinerat med design science research metodik. Trettiosju studenter bidrog till designen av dashboarden genom att ge feedback via en ansikte-till-ansikte verkstad och en gruppfeedbacksession under våren 2023. Implementationen evaluerades sedan genom fem semistrukturerade intervjuer baserade på Technology Acceptance modellen. Resultatet implicerar att studenter har en beetendemässig avsikt att använda verktyget i sina dag-för-dag universitetsstudier. Avhandlingen avslutas med diskussion om forskningsrelevans och framtida relaterade arbeten. Avhandlingen bidrar med en LAD fokuserad på studenters uppnående av kursmål och uppgiftintresse

Dieser Tagungsband beinhaltet die auf dem E-Learning Symposium 2012 an der Universität Potsdam vorgestellten Beiträge zu aktuellen Anwendungen, innovativen Prozesse und neuesten Ergebnissen im Themenbereich E-Learning. Lehrende, E-Learning-Praktiker und -Entscheider tauschten ihr Wissen über etablierte und geplante Konzepte im Zusammenhang mit dem Student-Life-Cycle aus. Der Schwerpunkt lag hierbei auf der unmittelbaren Unterstützung von Lehr- und Lernprozessen, auf Präsentation, Aktivierung und Kooperation durch Verwendung von neuen und etablierten Technologien.

Learning analytics focuses on using existing accumulated learning data through analysis related techniques to provide appropriate information to learners and facilitating learners to adjust their learning strategies (personalization and adaptation) in improving learning effectiveness. Through learning analytics, activities of teaching, learning, and management processes will be significantly changed. Although learning analytics has been considered one of the six critical trends (ebook, mobile learning, augmented reality, game-based learning, natural user interface, and learning analytics) of high education in the near future, there are only few studies focusing on exploring learning analytics related issues. To address this void, this thesis aims for analyzing and designing a personalized mobile learning analytics system that is a mobile application prototyping system developed by incorporating concepts of learning analytics and mobile learning. User requirements of the prototyping system are collected by database analysis (LMS platform), focus groups (users of mobile learning), and expert interviews (experts and practitioners in e-learning domain). Those collected requirements have been translated into system functionalities and then they have been appropriately implemented through adequate system development tools. Finally, the implemented prototyping system has been tested and validated by experts and practitioners in e-learning domain. Therefore, this study has significant contributions on conducting an in-depth system analysis and design relating to mobile learning with learning analytics and validating the feasibility of learning analytics by the prototyping approach. We suggest that academics and practitioners can conduct more in-depth research on investigating learning analytics related issues based on the findings of this study.

Der Einsatz digitaler Medien hat in der nationalen Hochschullehre Tradition. Lernmanagementsysteme (LMS), E-Learning, Blended Learning, etc. sind Schlagwörter im Hochschulalltag. Allerdings stellt sich die Frage, was LMS und Blended Learning im Zeitalter digitaler Vernetzung und der herangewachsenen Generation der “Digital Natives” leisten (können bzw. sollen)? Die Verbreitung neuer Technologien im Zusammenhang mit neuen Lehr- und Lernkonzepten wie OER, MOOCS, etc. macht zudem die Entwicklung von Analytics-Instrumenten erforderlich. Das ist auch im nationalen Diskurs von großem Interesse und legt neue Handlungsfelder für Hochschulen offen. Doch es stellt sich die Frage, warum Learning Analytics (LA) bzw. Academic Analytics (AA) bisher nur in einem geringfügigen Maße an deutschen Hochschulen erfolgreich zum Einsatz kommen und warum eine Nutzung insbesondere in LMS, wie zum Beispiel OPAL, nicht ohne Weiteres realisierbar erscheint. Hierzu sollen Einflussfaktoren, die die Implementierung von LA- und AA-Instrumenten hemmen, identifiziert und diskutiert werden. Aufbauend darauf werden erste Handlungsfelder vorgestellt, deren Beachtung eine verstärkte Einbettung von LA- und AA Instrumenten in LMS möglich machen soll.