Investigating Correlations of Automatically Extracted Multimodal Features and Lecture Video Quality

Ranking and recommendation of multimedia content such as videos is usually realized with respect to the relevance to a user query. However, for lecture videos and MOOCs (Massive Open Online Courses) it is not only required to retrieve relevant videos, but particularly to find lecture videos of high quality that facilitate learning, for instance, independent of the video's or speaker's popularity. Thus, metadata about a lecture video's quality are crucial features for learning contexts, e.g., lecture video recommendation in search as learning scenarios. In this paper, we investigate whether automatically extracted features are correlated to quality aspects of a video. A set of scholarly videos from a Mass Open Online Course (MOOC) is analyzed regarding audio, linguistic, and visual features. Furthermore, a set of cross-modal features is proposed which are derived by combining transcripts, audio, video, and slide content. A user study is conducted to investigate the correlations between the automatically collected features and human ratings of quality aspects of a lecture video. Finally, the impact of our features on the knowledge gain of the participants is discussed

EFAR-MMLA: An evaluation framework to assess and report generalizability of machine learning models in MMLA

Producción CientíficaMultimodal Learning Analytics (MMLA) researchers are progressively employing machine learning (ML) techniques to develop predictive models to improve learning and teaching practices. These predictive models are often evaluated for their generalizability using methods from the ML domain, which do not take into account MMLA’s educational nature. Furthermore, there is a lack of systematization in model evaluation in MMLA, which is also reflected in the heterogeneous reporting of the evaluation results. To overcome these issues, this paper proposes an evaluation framework to assess and report the generalizability of ML models in MMLA (EFAR-MMLA). To illustrate the usefulness of EFAR-MMLA, we present a case study with two datasets, each with audio and log data collected from a classroom during a collaborative learning session. In this case study, regression models are developed for collaboration quality and its sub-dimensions, and their generalizability is evaluated and reported. The framework helped us to systematically detect and report that the models achieved better performance when evaluated using hold-out or cross-validation but quickly degraded when evaluated across different student groups and learning contexts. The framework helps to open up a “wicked problem” in MMLA research that remains fuzzy (i.e., the generalizability of ML models), which is critical to both accumulating knowledge in the research community and demonstrating the practical relevance of these techniques.Fondo Europeo de Desarrollo Regional - Agencia Nacional de Investigación (grants TIN2017-85179-C3-2-R and TIN2014-53199-C3-2-R)Fondo Europeo de Desarrollo Regional - Junta de Castilla y León (grant VA257P18)Comisión Europea (grant 588438-EPP-1- 2017-1-EL-EPPKA2-KA

Automatic understanding of multimodal content for Web-based learning

Web-based learning has become an integral part of everyday life for all ages and backgrounds. On the one hand, the advantages of this learning type, such as availability, accessibility, flexibility, and cost, are apparent. On the other hand, the oversupply of content can lead to learners struggling to find optimal resources efficiently. The interdisciplinary research field Search as Learning is concerned with the analysis and improvement of Web-based learning processes, both on the learner and the computer science side. So far, automatic approaches that assess and recommend learning resources in Search as Learning (SAL) focus on textual, resource, and behavioral features. However, these approaches commonly ignore multimodal aspects. This work addresses this research gap by proposing several approaches that address the question of how multimodal retrieval methods can help support learning on the Web. First, we evaluate whether textual metadata of the TIB AV-Portal can be exploited and enriched by semantic word embeddings to generate video recommendations and, in addition, a video summarization technique to improve exploratory search. Then we turn to the challenging task of knowledge gain prediction that estimates the potential learning success given a specific learning resource. We used data from two user studies for our approaches. The first one observes the knowledge gain when learning with videos in a Massive Open Online Course (MOOC) setting, while the second one provides an informal Web-based learning setting where the subjects have unrestricted access to the Internet. We then extend the purely textual features to include visual, audio, and cross-modal features for a holistic representation of learning resources. By correlating these features with the achieved knowledge gain, we can estimate the impact of a particular learning resource on learning success. We further investigate the influence of multimodal data on the learning process by examining how the combination of visual and textual content generally conveys information. For this purpose, we draw on work from linguistics and visual communications, which investigated the relationship between image and text by means of different metrics and categorizations for several decades. We concretize these metrics to enable their compatibility for machine learning purposes. This process includes the derivation of semantic image-text classes from these metrics. We evaluate all proposals with comprehensive experiments and discuss their impacts and limitations at the end of the thesis.Web-basiertes Lernen ist ein fester Bestandteil des Alltags aller Alters- und Bevölkerungsschichten geworden. Einerseits liegen die Vorteile dieser Art des Lernens wie Verfügbarkeit, Zugänglichkeit, Flexibilität oder Kosten auf der Hand. Andererseits kann das Überangebot an Inhalten auch dazu führen, dass Lernende nicht in der Lage sind optimale Ressourcen effizient zu finden. Das interdisziplinäre Forschungsfeld Search as Learning beschäftigt sich mit der Analyse und Verbesserung von Web-basierten Lernprozessen. Bisher sind automatische Ansätze bei der Bewertung und Empfehlung von Lernressourcen fokussiert auf monomodale Merkmale, wie Text oder Dokumentstruktur. Die multimodale Betrachtung ist hingegen noch nicht ausreichend erforscht. Daher befasst sich diese Arbeit mit der Frage wie Methoden des Multimedia Retrievals dazu beitragen können das Lernen im Web zu unterstützen. Zunächst wird evaluiert, ob textuelle Metadaten des TIB AV-Portals genutzt werden können um in Verbindung mit semantischen Worteinbettungen einerseits Videoempfehlungen zu generieren und andererseits Visualisierungen zur Inhaltszusammenfassung von Videos abzuleiten. Anschließend wenden wir uns der anspruchsvollen Aufgabe der Vorhersage des Wissenszuwachses zu, die den potenziellen Lernerfolg einer Lernressource schätzt. Wir haben für unsere Ansätze Daten aus zwei Nutzerstudien verwendet. In der ersten wird der Wissenszuwachs beim Lernen mit Videos in einem MOOC-Setting beobachtet, während die zweite eine informelle web-basierte Lernumgebung bietet, in der die Probanden uneingeschränkten Internetzugang haben. Anschließend erweitern wir die rein textuellen Merkmale um visuelle, akustische und cross-modale Merkmale für eine ganzheitliche Darstellung der Lernressourcen. Durch die Korrelation dieser Merkmale mit dem erzielten Wissenszuwachs können wir den Einfluss einer Lernressource auf den Lernerfolg vorhersagen. Weiterhin untersuchen wir wie verschiedene Kombinationen von visuellen und textuellen Inhalten Informationen generell vermitteln. Dazu greifen wir auf Arbeiten aus der Linguistik und der visuellen Kommunikation zurück, die seit mehreren Jahrzehnten die Beziehung zwischen Bild und Text untersucht haben. Wir konkretisieren vorhandene Metriken, um ihre Verwendung für maschinelles Lernen zu ermöglichen. Dieser Prozess beinhaltet die Ableitung semantischer Bild-Text-Klassen. Wir evaluieren alle Ansätze mit umfangreichen Experimenten und diskutieren ihre Auswirkungen und Limitierungen am Ende der Arbeit

Camera-based estimation of student's attention in class

Two essential elements of classroom lecturing are the teacher and the students. This human core can easily be lost in the overwhelming list of technological supplements aimed at improving the teaching/learning experience. We start from the question of whether we can formulate a technological intervention around the human connection, and find indicators which would tell us when the teacher is not reaching the audience. Our approach is based on principles of unobtrusive measurements and social signal processing. Our assumption is that students with different levels of attention will display different non-verbal behaviour during the lecture. Inspired by information theory, we formulated a theoretical background for our assumptions around the idea of synchronization between the sender and receiver, and between several receivers focused on the same sender. Based on this foundation we present a novel set of behaviour metrics as the main contribution. By using a camera-based system to observe lectures, we recorded an extensive dataset in order to verify our assumptions. In our first study on motion, we found that differences in attention are manifested on the level of audience movement synchronization. We formulated the measure of ``motion lag'' based on the idea that attentive students would have a common behaviour pattern. For our second set of metrics we explored ways to substitute intrusive eye-tracking equipment in order to record gaze information of the entire audience. To achieve this we conducted an experiment on the relationship between head orientation and gaze direction. Based on acquired results we formulated an improved model of gaze uncertainty than the ones currently used in similar studies. In combination with improvements on head detection and pose estimation, we extracted measures of audience head and gaze behaviour from our remote recording system. From the collected data we found that synchronization between student's head orientation and teacher's motion serves as a reliable indicator of the attentiveness of students. To illustrate the predictive power of our features, a supervised-learning model was trained achieving satisfactory results at predicting student's attention

“The Bard meets the Doctor” – Computergestützte Identifikation intertextueller Shakespearebezüge in der Science Fiction-Serie Dr. Who.

A single abstract from the DHd-2019 Book of Abstracts.Sofern eine editorische Arbeit an dieser Publikation stattgefunden hat, dann bestand diese aus der Eliminierung von Bindestrichen in Überschriften, die aufgrund fehlerhafter Silbentrennung entstanden sind, der Vereinheitlichung von Namen der Autor*innen in das Schema "Nachname, Vorname" und/oder der Trennung von Überschrift und Unterüberschrift durch die Setzung eines Punktes, sofern notwendig