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To score or not to score. Factors influencing performance and feasibility of automatic content […]
Zesch, Torsten; Horbach, Andrea; Zehner, Fabian
Journal Article
| In: Educational Measurement: Issues and Practice | 2023
43438 Endnote
Author(s):
Zesch, Torsten; Horbach, Andrea; Zehner, Fabian
Title:
To score or not to score. Factors influencing performance and feasibility of automatic content scoring of text responses
In:
Educational Measurement: Issues and Practice, 42 (2023) 1, S. 44-58
DOI:
10.1111/emip.12544
URL:
https://onlinelibrary.wiley.com/doi/10.1111/emip.12544
Publication Type:
3a. Beiträge in begutachteten Zeitschriften; Beitrag in Sonderheft
Language:
Englisch
Keywords:
Antwort; Automatisierung; Bewertung; Einflussfaktor; Inhalt; Leistung; Text; Tool; Verfahren
Abstract (english):
In this article, we systematize the factors influencing performance and feasibility of automatic content scoring methods for short text responses. We argue that performance (i.e., how well an automatic system agrees with human judgments) mainly depends on the linguistic variance seen in the responses and that this variance is indirectly influenced by other factors such as target population or input modality. Extending previous work, we distinguish conceptual, realization, and nonconformity variance, which are differentially impacted by the various factors. While conceptual variance relates to different concepts embedded in the text responses, realization variance refers to their diverse manifestation through natural language. Nonconformity variance is added by aberrant response behavior. Furthermore, besides its performance, the feasibility of using an automatic scoring system depends on external factors, such as ethical or computational constraints, which influence whether a system with a given performance is accepted by stakeholders. Our work provides (i) a framework for assessment practitioners to decide a priori whether automatic content scoring can be successfully applied in a given setup as well as (ii) new empirical findings and the integration of empirical findings from the literature on factors that influence automatic systems' performance. (DIPF/Orig.)
DIPF-Departments:
Lehr und Lernqualität in Bildungseinrichtungen
Why do my thoughts feel so bad? Getting at the reciprocal effects of rumination and negative affect […]
Blanke, Elisabeth S.; Neubauer, Andreas B.; Houben, Marlies; Erbas, Yasemine; Brose, Annette
Journal Article
| In: Emotion | 2022
40873 Endnote
Author(s):
Blanke, Elisabeth S.; Neubauer, Andreas B.; Houben, Marlies; Erbas, Yasemine; Brose, Annette
Title:
Why do my thoughts feel so bad? Getting at the reciprocal effects of rumination and negative affect using dynamic structural equation modeling
In:
Emotion, 22 (2022) 8, S. 1773-1786
DOI:
10.1037/emo0000946
URL:
https://doi.apa.org/record/2021-21139-001
Publication Type:
3a. Beiträge in begutachteten Zeitschriften; Aufsatz (keine besondere Kategorie)
Language:
Englisch
Keywords:
Belgien; Datenanalyse; Datenerfassung; Dauer; Denken; Depression; Deutschland; Einstellung <Psy>; Emotion; Emotionaler Zustand; Fragebogenerhebung; Kognitive Prozesse; Messung; Messverfahren; Psychische Vorgänge; Stimmung; Stress; Strukturgleichungsmodell; Student; Tagesablauf; Wirkung
Abstract:
Rumination means to perseveratively think about one's negative feelings and problems. It is a response to affective distress that is often referred to as maladaptive emotion regulation. According to the response styles theory and control theory, rumination may further prolong and exacerbate affective distress. This means that rumination can be viewed as both an antecedent and an outcome of negative affect (NA), and vice versa. However, at the level of short-term dynamics, state rumination and NA have previously mainly been examined as two separate outcomes. To model the reciprocal within-person effects and hence, to match theoretical assumptions, we combined the two interrelated time series in one model using dynamic structural equation modeling (DSEM). Both effects (NA on subsequent rumination and rumination on subsequent NA) were modeled simultaneously while acknowledging the autoregressive nature of both states (inert properties). We used data from two experience sampling studies (NStudy 1 = 200 Belgian university students; NStudy 2 = 70 German university students). Participants were paged on smartphones several times a day (Study 1: 10; Study 2: 6) for several days (Study 1: 7; Study 2: 9-12). In both studies, we found evidence for reciprocal effects of NA and rumination, and both processes showed autoregressive relationships. Aside from central findings, higher levels of rumination were also associated with higher rumination inertia, pointing toward more habitual rumination also being associated with prolonged rumination. Together, using DSEM, we found reciprocal associations between rumination and NA, while providing new insights into the dynamics between the two processes. (DIPF/Orig.)
DIPF-Departments:
Bildung und Entwicklung
Integrating state dynamics and trait change. A tutorial using the example of stress reactivity and […]
Brose, Annette; Neubauer, Andreas B.; Schmiedek, Florian
Journal Article
| In: European Journal of Personality | 2022
41321 Endnote
Author(s):
Brose, Annette; Neubauer, Andreas B.; Schmiedek, Florian
Title:
Integrating state dynamics and trait change. A tutorial using the example of stress reactivity and change in well-being
In:
European Journal of Personality, 36 (2022) 2, S. 180-199
DOI:
10.1177/08902070211014055
URL:
https://journals.sagepub.com/doi/10.1177/08902070211014055
Publication Type:
3a. Beiträge in begutachteten Zeitschriften; Aufsatz (keine besondere Kategorie)
Language:
Englisch
Keywords:
Mehrebenenanalyse; Stress; Reaktion; Wirkung; Wohlbefinden; Emotionaler Zustand; Veränderung; Persönlichkeitsmerkmal; Messverfahren; Methode; Modellierung; Simulation; Strukturgleichungsmodell; Regressionsanalyse
Abstract:
Recent theoretical accounts on the causes of trait change emphasize the potential relevance of states. In the same vein, reactions to daily stress have been shown to prospectively predict change in well-being, speaking for the proposition that state dynamics can be a precursor to long-term change in more stable individual-differences characteristics. A common analysis approach towards linking state dynamics such as stress reactivity and change in some more stable individual differences characteristic has been a two-step approach, modeling state dynamics and trait change separately. In this paper, we elaborate on one-step procedures to simultaneously model state dynamics and trait change, realized in the multilevel structural equation modeling framework. We highlight three distinct advantages over the two-step approach which pre-exists in the methodological literature, and we disseminate these advantages to a larger audience. We target a readership of substantive researchers interested in the relationships between state dynamics and traits or trait change, and we provide them with a tutorial style paper on state-of-the-art methods on these topics. (DIPF/Orig.)
DIPF-Departments:
Bildung und Entwicklung
From sensor data to educational insights
Ruipérez-Valiente, José A.; Martínez-Maldonado, Roberto; Di Mitri, Daniele; Schneider, Jan
Journal Article
| In: Sensors | 2022
43621 Endnote
Author(s):
Ruipérez-Valiente, José A.; Martínez-Maldonado, Roberto; Di Mitri, Daniele; Schneider, Jan
Title:
From sensor data to educational insights
In:
Sensors, 22 (2022) 21, S. 8556
DOI:
10.3390/s22218556
URL:
https://www.mdpi.com/1424-8220/22/21/8556
Publication Type:
3a. Beiträge in begutachteten Zeitschriften; Aufsatz (keine besondere Kategorie)
Language:
Englisch
Keywords:
Datenanalyse; Datenerfassung; Künstliche Intelligenz; Lernprozess; Logdatei; Mensch-Maschine-Kommunikation; Messverfahren; Tool; Übersicht; Virtuelles Lernen
Abstract:
Technology is gradually becoming an integral part of learning at all levels of educational. This includes the now pervasive presence of virtual learning environments (VLEs) and the inclusion of interactive devices used or worn by learners or that are present in the physical classroom environment. These new technology-rich educational ecosystems have greatly facilitated data capture about learners. Thus, several research areas, such as learning analytics (LA), educational data mining (EDM), and artificial intelligence in education (AIED), have grown exponentially during the last decade, with multiple venues supporting this research [1]. However, the inferences about learning that can be made by solely analyzing trace data from VLEs are limited, since logged data do not commonly provide a complete view of the learning experience [2]. Therefore, research communities are moving beyond the data obtained from VLEs and other online tools by incorporating data from external sources such as sensors, pervasive devices, and computer vision systems. Within the context of education, this subfield is often denominated as multimodal learning analytics (MMLA) [3]; nevertheless, the use of these data sources is also common in broader research areas, such as affective computing (e.g., [4]) and human-computer interaction (HCI) (e.g., [5]). The promise is to augment and improve the extent and quality of the analysis that can be performed with these new data sources [6]. Moreover, many new sensor-based tools, such as sensor-based games [7] or realistic laboratories [8,9], are being built to support the educational process. The challenge is embedding sensors and resulting data representations in authentic educational settings in pedagogically meaningful and ethical ways [10]. This Special Issue (SI) invited publications that include approaches to converting data captured using sensors (e.g., cameras, smartphones, microphones, or temperature sensors), wearables (e.g., smart wristbands, watches, or glasses), or other Internet of Things (IoT) devices (e.g., interactive whiteboards, eBooks, or tablets) into meaningful educational insights. Moreover, it invited papers on tools, architectures, or frameworks to manage the orchestration of these sensors and IoT devices to improve education. The submitted articles had to appropriately explain how the inclusiveness of sensor devices can augment the analyses performed to improve teaching, learning, or the educational context in which the sensing it occurs (e.g., in classrooms, VLEs, or other educational spaces). This SI has focused on empirical case studies that fulfill the aforementioned criteria and experimental architectures, methodologies, frameworks, or survey papers. (DIPF/Orig.)
DIPF-Departments:
Informationszentrum Bildung
Performance of infit and outfit confidence intervals calculated via parametric bootstrapping
Silva Diaz, John Alexander; Köhler, Carmen; Hartig, Johannes
Journal Article
| In: Applied Measurement in Education | 2022
42707 Endnote
Author(s):
Silva Diaz, John Alexander; Köhler, Carmen; Hartig, Johannes
Title:
Performance of infit and outfit confidence intervals calculated via parametric bootstrapping
In:
Applied Measurement in Education, 35 (2022) 2, S. 116-132
DOI:
10.1080/08957347.2022.2067540
URL:
https://www.tandfonline.com/doi/full/10.1080/08957347.2022.2067540
Publication Type:
3a. Beiträge in begutachteten Zeitschriften; Aufsatz (keine besondere Kategorie)
Language:
Englisch
Keywords:
Item-Response-Theory; Rasch-Modell; Statistik; Methode; Verfahren; Stichprobe; Test; Analyse; Simulation
Abstract:
Testing item fit is central in item response theory (IRT) modeling, since a good fit is necessary to draw valid inferences from estimated model parameters. Infit and outfit fit statistics, widespread indices for detecting deviations from the Rasch model, are affected by data factors, such as sample size. Consequently, the traditional use of fixed infit and outfit cutoff points is an ineffective practice. This article evaluates if confidence intervals estimated via parametric bootstrapping provide more suitable cutoff points than the conventionally applied range of 0.8-1.2, and outfit critical ranges adjusted by sample size. The performance is evaluated under different sizes of misfit, sample sizes, and number of items. Results show that the confidence intervals performed better in terms of power, but had inflated type-I error rates, which resulted from mean square values pushed below unity in the large size of misfit conditions. However, when performing a one-side test with the upper range of the confidence intervals, the forementioned inflation was fixed. (DIPF/Orig.)
DIPF-Departments:
Lehr und Lernqualität in Bildungseinrichtungen
Zentrale quantitative Auswertungsverfahren
Becker, Michael; Maaz, Kai
Book Chapter
| Aus: Harring, Marius; Rohlfs, Carsten; Gläser-Zikuda, Michaela (Hrsg.): Handbuch Schulpädagogik | Münster: Waxmann | 2022
42715 Endnote
Author(s):
Becker, Michael; Maaz, Kai
Title:
Zentrale quantitative Auswertungsverfahren
In:
Harring, Marius; Rohlfs, Carsten; Gläser-Zikuda, Michaela (Hrsg.): Handbuch Schulpädagogik, Münster: Waxmann, 2022 , S. 948-958
Publication Type:
4. Beiträge in Sammelbänden; Lexika/Enzyklopädie o.ä.
Language:
Deutsch
Keywords:
Fehlende Daten; Quantitative Forschung; Schulforschung; Bildungsforschung; Statistische Methode; Modellierung; Stichprobe; Datenanalyse; Verfahren; Mehrebenenanalyse; Pfadanalyse; Skalierung; Längsschnittuntersuchung; Panel
Abstract:
[Im Beitrag wird] nach einer kurzen Darstellung der Gemeinsamkeiten statistischer Modellierung, die die Schul- und Bildungsforschung mit den meisten Sozialwissenschaften teilt, ein Überblick über die spezifischen Auswertungsverfahren gegeben, die sich vor allem in der quantitativen Bildungs- und Schulforschung häufig finden. Hierbei wird weniger auf die Darstellung spezifischer Kennwerte und ihrer Eigenschaften als auf die generelle Verwendung und den epistemologischen Zweck der Verfahren eingegangen. Der Fokus ist darauf gelegt, die wichtigsten statistischen Probleme und gegenwärtig gängigen analytischen Lösungen, wie sie in einer Vielzahl von Studien der Bildungs- und Schulforschung vorzufinden und zu adressieren sind, auf einer konzeptuellen Ebene zusammenzufassen und Hinweise für einen tieferen Einstieg in die Themen zu geben. Insofern sollen - nach einer kurzen Erläuterung der beiden grundlegenden Aspekte statistischer Modellierung (Deskriptive und Inferenzstatistik) im Allgemeinen - vier Aspekte quantitativer Auswertungen vorgestellt werden, wie sie unserer Meinung nach zentral für die quantitative Bildungs- und Schulforschung sind: (1) Hierarchische Datenstrukturen und Mehrebenenmodelle, (2) Pfadanalysen und längsschnittliche cross-lagged panel-Analysen, Fragen (3) fehlender Werte und (4) der Leistungsskalierung. (DIPF/Orig.)
DIPF-Departments:
Struktur und Steuerung des Bildungswesens
Längsschnitt in der Schulforschung
Becker, Michael; Schmiedek, Florian
Book Chapter
| Aus: Hascher, Tina; Idel, Till-Sebastian; Helsper, Werner (Hrsg.): Handbuch Schulforschung | Wiesbaden: Springer VS | 2022
40999 Endnote
Author(s):
Becker, Michael; Schmiedek, Florian
Title:
Längsschnitt in der Schulforschung
In:
Hascher, Tina; Idel, Till-Sebastian; Helsper, Werner (Hrsg.): Handbuch Schulforschung, Wiesbaden: Springer VS, 2022 , S. 195-217
DOI:
10.1007/978-3-658-24729-4_8
URL:
https://link.springer.com/chapter/10.1007/978-3-658-24729-4_8
Publication Type:
4. Beiträge in Sammelbänden; Lexika/Enzyklopädie o.ä.
Language:
Deutsch
Keywords:
Datenanalyse; Datenerfassung; Forschungsdesign; Längsschnittuntersuchung; Messverfahren; Methode; Modell; Schulforschung; Stichprobe; Theorie; Variable; Vergleich; Zeit
Abstract:
Der Beitrag gibt einen konzeptuellen Überblick über längsschnittliche Studiendesigns in der Schulforschung. Es wird skizziert, welche längsschnittlichen Designs sich grundsätzlich unterscheiden lassen, welche Untersuchungsperspektiven und statistischen Ansätze zur Verfügung stehen sowie welche Probleme bei längsschnittlichen Designs grundlegend und spezifisch in der Schulforschung auftreten. Es wird ein Ausblick auf die Bedeutung längsschnittlicher Studien für Beschreiben und Erklären in der Schulforschung gegeben.
DIPF-Departments:
Bildung und Entwicklung; Struktur und Steuerung des Bildungswesens
Erziehungswissenschaftliche Wissensgeschichte aus der Distanz, oder: Die Vermessung […]
Erdmann, Daniel; Vogel, Katharina
Book Chapter
| Aus: Oberdorf, Andreas (Hrsg.): Digital Turn und Historische Bildungsforschung: Bestandsaufnahme und Forschungsperspektiven | Bad Heilbrunn: Klinkhardt | 2022
43408 Endnote
Author(s):
Erdmann, Daniel; Vogel, Katharina
Title:
Erziehungswissenschaftliche Wissensgeschichte aus der Distanz, oder: Die Vermessung erziehungswissenschaftlichen "Grundwissens" durch Methoden aus dem Bereich des "distant reading" Ein Werkstattbericht
In:
Oberdorf, Andreas (Hrsg.): Digital Turn und Historische Bildungsforschung: Bestandsaufnahme und Forschungsperspektiven, Bad Heilbrunn: Klinkhardt, 2022 , S. 17-32
DOI:
10.25656/01:24850
URN:
urn:nbn:de:0111-pedocs-248503
URL:
https://www.pedocs.de/frontdoor.php?source_opus=24850
Publication Type:
4. Beiträge in Sammelbänden; Tagungsband/Konferenzbeitrag/Proceedings
Language:
Deutsch
Keywords:
18. Jahrhundert; 19. Jahrhundert; 20. Jahrhundert; 21. Jahrhundert; Analyse; Automatisiertes System; Bildungsgeschichte; Computerunterstütztes Verfahren; Digitale Medien; Digitalisierung; Erziehungswissenschaft; Grundwissen; Historische Bildungsforschung; Historische Quelle; Längsschnittuntersuchung; Methode; Modellierung; Projekt; Wissenschaftsforschung
Abstract (english):
Der vorliegende Werkstattbericht soll am Beispiel unseres DFG-geförderten Projektes "EWiG. Was ist erziehungswissenschaftliches Grundwissen? Wissensgeschichtliche Analysen 1780-2000" Möglichkeiten und Grenzen von distant reading in der historischen Wissens(geschichts)- und Wissenschaftsforschung zur Diskussion stellen, in dem wir uns eher konventionellen und vermeintlich gut bekannten Genres und Datenkorpora (Lehrbücher, Klassiker und Geschichten der Pädagogik zwischen 1780 und 2000) auf bisher wenig verbreiteten Wegen und mit entsprechenden Werkzeugen annähern. Wir beginnen mit einem rudimentären Überblick über Ziele und Quellen des Projektes; im Anschluss stellen wir nach kurzen Erläuterungen der jeweils zur Anwendung gekommenen methodischen Verfahren erste Ergebnisse unserer Analysen vor, bevor wir mit einem Fazit zur Frage schließen, ob und inwiefern das alles als digital turn zu begreifen ist. (DIPF/Orig.)
DIPF-Departments:
Bibliothek für Bildungsgeschichtliche Forschung
Vertrauensbildende Maßnahmen zu Open Educational Resources (OER)
Fahrer, Sigrid
Book Chapter
| Aus: Fuchs, Eckhardt; Otto, Marcus (Hrsg.): In education we trust?: Vertrauen in Bildung und Bildungsmedien | Göttingen: V&R unipress | 2022
43036 Endnote
Author(s):
Fahrer, Sigrid
Title:
Vertrauensbildende Maßnahmen zu Open Educational Resources (OER)
In:
Fuchs, Eckhardt; Otto, Marcus (Hrsg.): In education we trust?: Vertrauen in Bildung und Bildungsmedien, Göttingen: V&R unipress, 2022 (Bildungsmedienforschung. Studien des Leibniz-Instituts für Bildungsmedien, 153), S. 233-245
DOI:
10.14220/9783737015097.233
URL:
https://www.vr-elibrary.de/doi/10.14220/9783737015097.233
Publication Type:
4. Beiträge in Sammelbänden; Sammelband (keine besondere Kategorie)
Language:
Deutsch
Keywords:
Digitalisierung; Kontrolle; Künstliche Intelligenz; Lehrmittel; Lernmittel; Open Educational Resources; Qualitätssicherung; Unterrichtsmaterial; Verfahren; Vertrauen
DIPF-Departments:
Informationszentrum Bildung
Effective use of learner-generated data in teacher training activities
Drachsler, Hendrik
Working Papers
| 2022
43208 Endnote
Author(s):
Drachsler, Hendrik
Title:
Effective use of learner-generated data in teacher training activities
Published:
Brussels: European Schoolnet Academy, 2022
URL:
http://www.eun.org/documents/411753/817341/Effective+Use+of+Learner-Generated+Data+in+Teacher+Training+Activities+v3.pdf/6e1bfeb2-b981-4170-a251-01e496173d08
Publication Type:
5. Arbeits- und Diskussionspapiere; Handreichung für die Praxis
Language:
Englisch
Keywords:
Analyseverfahren; Computerprogramm; Datenanalyse; Datenschutz; Datenschutzgesetz; Ethik; Learning Analytics; Lehrerausbildung; Technologie; Trainingsprogramm
Abstract:
This report contains an analysis of the effective use of learner-generated data with a specific focus on teacher training. The use of learner-generated data in research is coined Learning Analytics. So far very little is published about learner-generated data in the context of teacher training. The following report aims to introduce teacher trainers, policymakers and practitioners to learning analytics. It defines learning analytics and describes the core dimensions of it, such as stakeholders, objectives, data, technologies, ethics & privacy, and required competencies to exploit the benefits of learning analytics. After a comprehensive introduction to learning analytics, the report shows practical examples of learning analytics applications. It describes how learner-generated data can be identified, captured, and visualised on learner analytics dashboards. It also provides examples of public-funded and commercial applications of learning that take advantage of learning analytics in schools these days. Finally, the report outlines a potential teacher training program inspired by university master programs on learning analytics. It also suggests two training scenarios (a low-tech and a high-tech scenario) that could be used for the training of teachers on learner-generated data. (DIPF/Orig.)
DIPF-Departments:
Informationszentrum Bildung
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