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With adequate support for the learner, errors can have high learning potential. This study investigates rather unsuitable action patterns of teachers in dealing with errors. Teachers rarely investigate the causes that evoke the occurrence of individual students’ errors, but instead often change addressees immediately after an error occurs. Such behavior is frequent in the classroom, leaving unexploited, yet important potential to learn from errors. It has remained unexplained why teachers act the way they do in error situations. Using video-stimulated recalls, I investigate the reasons for teachers’ behavior in students’ error situations by confronting them with recorded episodes from their own teaching. Error situations are analyzed (within-case) and teachers’ beliefs are classified in an explanatory model (cross-case) to illustrate patterns across teachers. Results show that teachers refer to an interaction of student attributes, their own attributes, and error attributes when reasoning their own behavior. I find that reference to specific attributes varies depending on the situation, and so do the described reasons that led to a particular behavior as a spontaneous or more reflective decision.
In the digital condition, social networks are part of the everyday practices that produce constructions of space. To include this phenomenon in the Geography classroom, the corresponding knowledge that teachers require and how to measure it need to be determined. For this purpose, a specific instrument is necessary. We analyse constructions of space as a teacher knowledge domain through the lens of the TPACK model and deduce pedagogical and technological knowledge. We then combine these results with available TPACK survey instruments. The product is a self-assessment survey for Geography teachers on constructions of space and social networks. Scale consistency measures and item discrimination yielded good results. Initial results indicate technological and pedagogical knowledge to be greater than content knowledge. The instrument we present is valid for determining future Geography teachers’ TPACK more broadly, and can reveal implications for interventions in Geography teacher education.
Digital spatial processes have been widely explored and investigated in subject-specific geographic research. So far, however, this research has not been sufficiently reflected in classrooms or teacher education, and remains unconnected to notions of geographical digital literacy. Viral constructions of space – realities shaped in everyday life that are experienced and (re-)produced by students and teachers alike through social media – present an opportunity for Geography education to adapt to the digital society. This paper attempts to connect viral constructions of space, the digital society and the knowledge teachers need to include viral constructions of space in the classroom using Mishra and Koehler’s (2006) TPACK model, a well-established means for summarizing teachers’ technological, pedagogical and content knowledge for a specific topic. The paper focuses on content knowledge, identifies five sub-types of viral constructions of space, and extracts nine descriptors of teachers’ content knowledge. By focusing on content knowledge, the paper presents a starting point for future investigations of pedagogical and technological teacher knowledge as well as their intersections. It also raises awareness of viral constructions of space as both a new essential topic in the Geography classroom and a phenomenon already shaping learning environments for spatial acquisition.
Geometry is part of the core of mathematics. It has been relevant ever since people have interacted with nature and its phenomena. Geometry’s relevance to the teaching and learning of mathematics can be emphasized, too. Nevertheless, a current potential shift in the topics of mathematics education to the detriment of geometry might be emerging. That is, other topics related to mathematics are seeming to grow in importance in comparison to geometry. Despite this, or perhaps because of it, geometry is an important component of current research in mathematics education. In the literature review, we elaborate relevant foci on the basis of current conference proceedings. By means of about 50 journal articles, five main topics are elaborated in more detail: geometric thinking and practices, geometric contents and topics, teacher education in geometry, argumentation and proof in geometry, as well as the use of digital tools for the teaching and learning of geometry. Conclusions and limitations for current and future research on geometry are formulated at the end of the article. In particular, the transfer to the practices of geometric teaching is explored on the basis of the elaborated research findings in order to combine both aspects of the teaching and learning of geometry.
Die Digitalisierung aller Lebensbereiche stellt die Bildungsarbeit in Schulen, Kirchgemeinden und in der Gedenkstättenpädagogik vor neue Herausforderungen. Der Artikel beschreibt vor diesem Hintergrund Digitalisierungsprojekte an der Goethe-Universität in Frankfurt am Main, die sich mit virtuellen Realitäten, ambivalenten Narrativen und fiktiven Entscheidungssituationen beschäftigen. Besondere Aufmerksamkeit erhalten dabei das Judentum und die bildungstheoretischen Anliegen einer Didaktik der Multiperspektivität.
Highlights
• Transparency of design, reference frames and support for action were found to support students' sense-making of LA dashboards.
• The higher the overall SRL score, the more relevant the three factors were perceived by learners.
• Learner goals affect how relevant students find reference frames.
• The SRL effect on the perceived relevance of transparency depends on learner goals.
Abstract
Unequal stakeholder engagement is a common pitfall of adoption approaches of learning analytics in higher education leading to lower buy-in and flawed tools that fail to meet the needs of their target groups. With each design decision, we make assumptions on how learners will make sense of the visualisations, but we know very little about how students make sense of dashboard and which aspects influence their sense-making. We investigated how learner goals and self-regulated learning (SRL) skills influence dashboard sense-making following a mixed-methods research methodology: a qualitative pre-study followed-up with an extensive quantitative study with 247 university students. We uncovered three latent variables for sense-making: transparency of design, reference frames and support for action. SRL skills are predictors for how relevant students find these constructs. Learner goals have a significant effect only on the perceived relevance of reference frames. Knowing which factors influence students' sense-making will lead to more inclusive and flexible designs that will cater to the needs of both novice and expert learners.
Highlights
• Students have limited concerns about privacy in learning analytics (LA).
• Students' privacy concerns in LA vary across countries.
• Culture shapes students' privacy concerns in LA.
• Power distance, uncertainty avoidance and masculinity affect privacy concerns in LA.
• Cultural values should be considered in LA privacy management.
Abstract
Applications of learning analytics (LA) can raise concerns from students about their privacy in higher education contexts. Developing effective privacy-enhancing practices requires a systematic understanding of students’ privacy concerns and how they vary across national and cultural dimensions. We conducted a survey study with established instruments to measure privacy concerns and cultural values for university students in five countries (Germany, South Korea, Spain, Sweden, and the United States; N = 762). The results show that students generally trusted institutions with their data and disclosed information as they perceived the risks to be manageable even though they felt somewhat limited in their ability to control their privacy. Across the five countries, German and Swedish students stood out as the most trusting and least concerned, especially compared to US students who reported greater perceived risk and less control. Students in South Korea and Spain responded similarly on all five privacy dimensions (perceived privacy risk, perceived privacy control, privacy concerns, trusting beliefs, and non-self-disclosure behavior), despite their significant cultural differences. Culture measured at the individual level affected the antecedents and outcomes of privacy concerns. Perceived privacy risk and privacy control increase with power distance. Trusting beliefs increase with a desire for uncertainty avoidance and lower masculinity. Non-self-disclosure behaviors rise with power distance and masculinity and decrease with more uncertainty avoidance. Thus, cultural values related to trust in institutions, social equality and risk-taking should be considered when developing privacy-enhancing practices and policies in higher education.
Sleep and Attention-Deficit/Hyperactivity Disorder (ADHD) have repeatedly been found to be associated with each other. However, the ecological validity of daily life studies to examine the effect of sleep on ADHD symptoms is rarely made use of. In an ambulatory assessment study with measurement burst design, consisting of three bursts (each 6 months apart) of 18 days each, 70 German schoolchildren aged 10–12 years reported on their sleep quality each morning and on their subjective ADHD symptom levels as well as their sleepiness three times a day. It was hypothesized that nightly sleep quality is negatively associated with ADHD symptoms on the inter- as well as the intraindividual level. Thus, we expected children who sleep better to report higher attention and self-regulation. Additionally, sleepiness during the day was hypothesized to be positively associated with ADHD symptoms on both levels, meaning that when children are sleepier, they experience more ADHD symptoms. No association of sleep quality and ADHD symptoms between or within participants was found in multilevel analyses; also, no connection was found between ADHD symptoms and daytime sleepiness on the interindividual level. Unexpectedly, a negative association was found on the intraindividual level for ADHD symptoms and daytime sleepiness, indicating that in moments when children are sleepier during the day, they experience less ADHD symptoms. Explorative analyses showed differential links of nightly sleep quality and daytime sleepiness, with the core symptoms of inattention and hyperactivity/impulsivity, respectively. Therefore, future analyses should take the factor structure of ADHD symptoms into account.
Highlights
• Pre-service teachershave stereotypes towards pupils with autism, Down syndrome and dyslexia.
• Pupils with Down syndrome, autism and dyslexia are associated with distinctive stereotypes.
• These stereotypes can be classified in three resp. four different dimensions.
Abstract
Stereotypes about pupils with special educational needs are prevalent both in society and among pre- and in-service teachers. However, little is known about the specific stereotypes pre-service teachers associate with autistic pupils, pupils with Down syndrome, and pupils with dyslexia. We explored these in two studies. Study 1 (N=13) involved qualitative interviews to identify potential stereotype content. Study 2 (N=213) used these findings to create a questionnaire to quantify these stereotypes. We found distinct stereotypes associated with all three groups of pupils. For successful inclusion, teachers must recognize the uniqueness of each pupil, including those with different diagnoses.
Current deep learning methods are regarded as favorable if they empirically perform well on dedicated test sets. This mentality is seamlessly reflected in the resurfacing area of continual learning, where consecutively arriving data is investigated. The core challenge is framed as protecting previously acquired representations from being catastrophically forgotten. However, comparison of individual methods is nevertheless performed in isolation from the real world by monitoring accumulated benchmark test set performance. The closed world assumption remains predominant, i.e. models are evaluated on data that is guaranteed to originate from the same distribution as used for training. This poses a massive challenge as neural networks are well known to provide overconfident false predictions on unknown and corrupted instances. In this work we critically survey the literature and argue that notable lessons from open set recognition, identifying unknown examples outside of the observed set, and the adjacent field of active learning, querying data to maximize the expected performance gain, are frequently overlooked in the deep learning era. Hence, we propose a consolidated view to bridge continual learning, active learning and open set recognition in deep neural networks. Finally, the established synergies are supported empirically, showing joint improvement in alleviating catastrophic forgetting, querying data, selecting task orders, while exhibiting robust open world application.