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Künstliche Intelligenz versucht, die menschliche Problemlösefähigkeit nachzubilden. Doch „denkende“ technische Systeme sind nicht automatisch in der Lage, sich in Menschen hineinzuversetzen und der Situation angemessen mit ihnen zu interagieren. Vor zwölf Jahren sind Forschende der Universität Bielefeld mit dem Exzellenzcluster CITEC angetreten, Technologie zu entwickeln, die sich an den Menschen anpasst.Auf der CITEC-Konferenz 2019 am 24. und 25. Oktober stellen die Wissenschaftlerinnen und Wissenschaftler die Ergebnisse ihrer Arbeit vor und diskutieren, wie sie ihren Ansatz der Kognitiven Interaktionstechnologie in Zukunft weiterentwickeln. Die bekannten Robotikforscher Professor Dr. Hiroshi Ishiguro aus Japan und Professor Dr. Giulio Sandini aus Italien halten die Keynote-Vorträge. Beide Vorträge sind öffentlich.

Die Fachtagung verfolgt das Ziel, ein Forum zu schaffen, bei dem Digitalisierung in der Fort- und Weiterbildung in pädagogisch-pflegerischen Arbeitsfeldern im Mittelpunkt steht. Für die Teilnehmenden wird ein interaktives und kreatives Tagungsformat kreiert, das Möglichkeiten zum intensiven Austausch untereinander sowie mit Expert_innen aus Forschung und Praxis bereithält, um Anregungen für die eigene Arbeit zu erhalten. Die Tagung richtet sich an Anbieter_innen aus der Weiterbildung, an pädagogisch-pflegerische Fachkräfte und ihre Klient_innen, an Wohlfahrtsverbände sowie an alle weiteren Interessierten. In den Workshops werden verschiedene digitale Medien und ihre Nutzungsweisen ausprobiert und eigene kleine Projekte gestaltet. Tag 1 richtet sich dezidiert an alle Teilnehmenden und wird barrierefrei gestaltet. Anmeldefrist: 6. November 2019

Der Berufsbildungskongress steht unter dem Motto "Digitalisierung jenseits des Kabels". Foren bieten Informationen zur pädagogischen Bearbeitung des digitalen Wandels. Was können berufliche Bildung und Schule zur Entwicklung der digitalen Gesellschaft beitragen?

Unter dem Motto „Unsere Welt ist bunt“ soll während der Lingener Kinderbuchwoche mit vielen Lesungen, Kreativangeboten und Aktionen Lesebegeisterung geweckt und gefördert werden. Getreu dem Zitat des französischen Schriftstellers André Gide „Das Buch, ein Haufen toter Buchstaben? Nein, ein Sack voller Samenkörner“, wollen die Initiatoren der Buchwoche dazu beitragen, diese Samenkörner in der ganzen Stadt zu verbreiten. Viele Lingener Prominente werden an den Vormittagen wieder in Lingener Grundschulen aus ihren Lieblingskinderbüchern vorlesen. Die Kunstschule Lingen bietet Schülerinnen und Schülern die Möglichkeit, zum Thema „Freundschaft, Vielfalt und Integration“ kreativ zu werden. Bekannte Autorinnen und Autoren werden in Lingen zu Gast sein und ihre Bücher vorstellen. Auch Bilderbuchkinos, Kamishibai und ein Bilderbuch-Slam stehen auf dem Programm. Zum Abschluss findet am 10. November auf dem Universitätsplatz ein Kinderbuchfest statt. Der Eintritt zu fast allen Veranstaltungen ist frei. Das Programm ist online verfügbar.

Die offene Gesellschaft braucht die Wissenschaft. Diese steht jedoch unter Druck. Die Anforderungen an sie sind so groß wie vielfältig: Sie soll in Zeiten enormer politischer Spannungen Lösungen für die großen gesellschaftlichen Fragen finden. Sie soll verständlich im Diskurs stattfinden und als vernünftige Gegenspielerin zu den „einfachen Lösungen“ Wirkung entfalten. Von ihr wird allerorts ein Transfer von Erkenntnissen in die Praxis verlangt, gleichzeitig schränkt Ökonomisierung ihre Spielräume und Grundlagen ein.Ziel dieser Konferenz ist es, gemeinsam zu erörtern, wie Wissenschaft unter veränderten gesellschaftlichen Rahmenbedingungen Verantwortung für eine offene Gesellschaft übernehmen und ihrer Rolle als Aufklärungsinstanz gerecht werden kann.  

Das SAE Institute Bochum ist ein privates Ausbildungsinstitut und bietet praxisorientierte Ausbildungen und Studiengänge in den Bereichen Audio, Film, Web und Games an. Im Gegensatz zu den meisten anderen Studienanbietern setzt SAE Institute erfolgreich auf das Konzept "learning by doing", denn nur was man sich selbst erarbeitet hat, hat man auch verstanden. Am 30. November können sich Interessierte in gemütlicher Atmosphäre einen Eindruck von den Ausbildungen und Studiengängen am SAE Institute verschaffen und sich mit Studierenden, Lehrenden und der Schulleitung austauschen und Fragen klären.

Die Brüder Grimm Berufsakademie Hanau, Anbieter des deutschlandweit einzigartigen ausbildungsintegrierten (Industriekaufmann-/frau) Studienganges "Innovationsmanagement" lädt interessierte Unternehmen und Studieninteressierte am Dienstag, 22.10.2019 von 17 - 20 Uhr herzlich zu unserem ersten Inno Treff der Brüder Grimm Berufsakademie nach Hanau ein. Neben einem Vortrag von Frau Prof. Simon, Akademieleiterin und Frau Katharina Berger, ehemalige Innovationsexpertin der Deutschen Bank zum Thema Innovationsmanagement, wird Herr Deberle Geschäftsführer der Kroeplin GmbH und Ausbildungsbetrieb für den Studiengang Innovationsmanagement von seinen Erfahrungen berichten. Die Teilnahme ist kostenfrei.

Die Offene Digitalisierungsallianz Pfalz lädt am Freitag, den 29. November 2019 Lehrkräfte zur Tagung Bildung.Regional.Digital ein. Sie gibt dort einen umfassenden Überblick über die aktuellen Trends und Möglichkeiten in der digitalen Lehre. Fragen rund um den Einsatz von Smartphones und Tablets im Unterricht sowie konkrete Mehrwerte der Digitalisierung des Unterrichts werden im Zentrum der Veranstaltung stehen und von Wissenschaftlern der TU Kaiserslautern und der Hochschule Kaiserslautern beantwortet werden.  Welche Mehrwerte bieten „digitale Bildungswerkzeuge“ ganz konkret an Schulen, Ausbildungseinrichtungen, Hochschulen und in Unternehmen? Wie kann man ein Smartphone sinnvoll im Unterricht und in der betrieblichen Bildung einsetzen? Wird das Schulbuch bald durch das Tablet ersetzt? Was können Lehrer unternehmen, wenn ihre Schüler digital bereits weiter sind als sie selbst?Die Veranstaltung ist als Lehrerfort- und Weiterbildung anerkannt.

"Recognizing that higher education is facing a number of challenges brought about by today’s complex societies, the IAU 2019 International Conference is set to discuss the future role of higher education against a backdrop of two major global phenomena: the impact of rapid technological advancements on higher education; and secondly, the urgent need to create more sustainable societies." [Abstract: Site editor’s information]

Der Kongress der ConSozial bietet Fach- und Führungskräften Vorträge aus 9 Themenfeldern. Das Spektrum reicht von der Kinder- und Jugendhilfe über Personalentwicklung bis hin zu Hilfen für ältere Menschen. In Tandem-Vorträgen berichten soziale Organisationen und gewerbliche Unternehmen von gemeinsam realisierten Projekten.Beim parallel stattfindenden, zweitägigen KITA-Kongress der ConSozial kommen alle auf ihre Kosten, die im frühpädagogischen Bereich arbeiten: Von der Praxis für die Praxis gestaltete Beiträge bieten Raum für Austausch und Diskussion. Im Messebereich der ConSozial stellen Aussteller ihr Serviceangebot vor. Besucher bekommen einen Marktüberblick sowie praxisrelevante Lösungsansätze an die Hand. Neben dem Besuch von Vorträgen, Workshops oder der Messe bleibt genügend Zeit, sich fachlich auszutauschen und Visionen zu entwickeln – auch über das eigene Arbeitsfeld hinaus.

Qualifizierte Übersetzerinnen und Übersetzer, die sich in sechs Monaten im Online-Lehrgang zusätzlich zum staatlich geprüften Dolmetscher für Englisch qualifizieren möchten, lädt die Übersetzer- und Dolmetscherschule Köln zu einem Live-Chat ein. Der Informationsabend im Web findet statt am 24. Oktober 2019 um 19 Uhr. Dann stellt Schulleiter Dr. Jerry Neeb-Crippen das Online-Kursangebot „staatlich geprüfter Dolmetscher“ vor und beantwortet Fragen. Der neue Kurs startet am 1. Januar 2020. Die Schule bittet um Anmeldung zum Informationsabend per Mail oder telefonisch.

Der Jenaer Lesemarathon feiert Jubiläum: Vom 24. Oktober bis 14. November laden die Ernst-Abbe-Bücherei Jena und der Lese-Zeichen e.V. zur 25. Ausgabe ein. Neben Lesungen unter anderem mit Saša Stanišic, Isabel Bogdan, David Wagner und Daniela Krien stehen ein Südosteuropa-Abend und die Veranstaltung „Vom Atem der Städte“ - ein Beitrag zum Bauhaus-Jubiläum - auf dem Programm. In der Villa Rosenthal wird die Ausstellung „Johannes Bobrowskis Orte in den Fotografien von Dmitry Vyshemirsky“ gezeigt. Veranstaltungorte sind neben der Ernst-Abbe-Bücherei am Carl-Zeiß-Platz 10, die Friedrich-Schiller-Universität, das Volksbad, die Mensa Philosphenweg, Schillers Gartenhaus, die Cafeteria „Zur Rosen“ und die Villa Rosenthal.

Eine knappe Woche lang werden unterschiedliche Themen beleuchtet, die für die Studienwahl wichtig sind. Das geschieht in Technik-Workshops, Laborexperimenten, Institutsbesuchen und Gesprächen mit Studierenden verschiedener Fächer, mit einem Studienberater sowie mit Wissenschaftlerinnen und Wissenschaftlern aus Industrie und Forschung.Im Mittelpunkt des Kurses stehen die Interessen, Stärken und Ziele der TeilnehmerInnen, die sie in einem zweieinhalbtägigen Training mit Profis zusammen erarbeiten. Die TeilnehmerInnen erhalten Einblicke in verschiedene MINT-Fächer, lernen viele Gleichgesinnte kennen, planen weitere Schritte auf dem Weg ins Studium und knüpfen wertvolle Kontakte.

Diversity Meets Tech Innovation – this is not only a fantastic theme for this year’s moinblockchain in our Hanseatic city, but also a major task for us as part of society: Just like digitalization changes and shapes our everyday life, women must be included equally in digital development and design processes. Today, this is one of our biggest political challenges for gender equality!

Unter dem Titel „Gleiches Ziel, vielfältige Ansätze – Strategien und regionale Herausforderungen am Übergang Schule – Beruf“ lädt die Hessenweite OloV-Koordination zu einem Austausch über die Ländergrenzen hinweg nach Limburg ein.

"The 2019 conference theme for The 11th Asian Conference on Education is “Independence & Interdependence”, and invites reflections on the desirability, extent and limits of our individual independence and autonomy, of that of our students, and of the institutions and structures within which we work, teach and learn. We do not educate, and are not educated in vacuums, but in such contexts and constraints as families, groups, and societies; of nations and cultures; of identities and religions; and of political and financial realities."[Abstract: Site editor’s information]

ECER 2020 suggests that the role of educational research is to establish the position or place of education in the recurrent debates and tensions between the local and global dimensions of life and help to connect and reconnect communities. Participants are invited to interrogate this contention, in order to examine the potential of educational research to (re)connect communities across Europe and beyond.

Seit 2004 veranstaltet die DGfE jedes Jahr die Summer School zu qualitativen und quantitativen Forschungsmethoden für junge Erziehungswissenschaftlerinnen und Erziehungswissenschaftler. Die Summer School richtet sich aber auch an Wissenschaftler*innen anderer geistes- und sozialwissenschaftlicher Disziplinen. Eröffnet wird die Summer School mit einem Vortrag, in dem namhafte Wissenschaftler*innen ihre aktuellen Projekte vorstellen und auf forschungsbezogene Daten und Befunde eingehen. Auf dieser Basis entwickeln sich Kontakte zu etablierten Wissenschaftler*innen, weitverzweigte Netzwerke und gemeinsame Projekte.

Seit 2005 trägt die Studie zur Entwicklung von Ganztagsschulen (StEG) maßgeblich zur Weiterentwicklung der Ganztagsschullandschaft bei. Nach 15 Jahren Forschung zieht die StEG-Tagung Bilanz und möchte Vertreterinnen und Vertretern aus Schulpraxis, Wissenschaft und Politik Perspektiven der Zusammenarbeit aufzeigen. Im Fokus der Veranstaltung steht, was StEG für die Weiterentwicklung der Ganztagsschullandschaft geleistet hat. Nach dem Hauptvortrag "15 Jahre Ganztagsschulforschung im Rahmen von StEG – Zentrale Befunde und offene Fragen" von Prof. Dr. Ludwig Stecher (Justus-Liebig-Universität Gießen), einem der Hauptverantwortlichen der Studie, werden die Inhalte seines Vortrags in dialogischen Formaten vertieft. Im Anschluss werden in vier parallel stattfindenden Sessions aktuelle Ergebnisse der StEG-Teilstudien vorgestellt und in vier parallelen Foren besonders kontroverse Themen der Ganztagsschuldebatte erörtert. Den Abschluss der Tagung bildet ein Ausblick in Form einer Podiumsdiskussion: Wie kann die Forschung die Ganztagsschulentwicklung künftig weiter unterstützen?

Die diesjährige AKSB-Jahrestagung befasst sich mit digitaler Ethik als Aufgabe der politischen Bildung. In Impulsvorträgen werden Expert/-innen aus Wissenschaft und Praxis verschiedene Schwerpunkte näher behandeln, etwa die Zusammenarbeit von Mensch und Maschine, den Umgang mit Algorithmen und Impulse für die politische Bildungsarbeit. In einem Barcamp werden weitere Themen behandelt. Teil der Jahrestagung ist eine gemeinsame Fachtagung der AKSB und KEB Deutschland zum Thema „Digitalität als Chance für die Demokratie“.

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Abstract

A cyber-flipped course was conducted with the flipped classroom pedagogy by using a wholly online approach for all learning activities in asynchronous and synchronous class sessions. Literature suggests that traditional flipped courses can effectively enhance students’ learning outcomes in comparison to non-flipped courses. However, conducting all asynchronous and synchronous learning activities using a wholly online approach has not been reported. This paper aimed to investigate how student engagement in four different types of learning activities affects their learning outcomes in a cyber-flipped course. Results show that the learning activities with the flipped classroom pedagogy can be successfully implemented and conducted in a wholly online course along with time and space flexibility for learners. This study also found that students who watched more pre-recorded video lectures tended to participate in the synchronous learning activities more actively and obtained a higher semester grade; higher completion of asynchronous learning activities had benefited students’ understanding of the learning concepts. Furthermore, students who had a high level of readiness by attending synchronous class sessions on time and keeping their webcams activated had more frequent and proactive interactions with their peers and instructor.

Abstract

The recent movement to integrate the flipped classroom model into higher education has resulted in significant changes that affected both teaching and learning practices in different ways. After almost a decade of research on the flipped classroom model, different emergent outcomes have been reported in a domain specific context. To gain a comprehensive understanding of the flipped classroom implementation in a university context, a review of the literature on the use of flipped classroom in a university context was conducted. This study was guided by interpreting the previous research findings according to the domain of utilization, opportunities, challenges, and extensions to the conventional flipped classroom model. This study found that the utilization of flipped classroom in various disciplines is mainly advocated to promote students’ engagement, metacognition, attitude, performance, understanding, and achievement, as well as other learning outcomes. The key challenges of this method, shared across all disciplines, were devoted to the length of the video/digital materials and time required for instructors to prepare the learning materials and for students to master it. Recommendations for policy makers and other crucial insights for the future studies were highlighted.

Abstract

Multitasking refers to the simultaneous execution of two or more tasks. Perceived multitasking superiority of the digital natives and gifted students in the popular education literature need to be investigated with robust studies. In this regard, the effect of different multitasking scenarios on multimedia learning was investigated with 93 gifted and 121 non-gifted middle school students. The respondents were assigned randomly to three different scenarios: Monotasking (i.e. watching an instructional video without interruption), concurrent multitasking (i.e. texting during an instructional video) and sequential multitasking (i.e. watching instructional and distractive videos successively). In addition to content learning, the students’ scores on topic interest, daily multitasking habits, subjective cognitive load and working memory capacity were considered. Working memory capacity correlated positively with learning outcomes. After it was included as a covariate, the results of a two-way between-groups ANCOVA revealed that multitasking conditions interfered with learning. Gifted students were consistently more successful than non-gifted students, but suffered during concurrent multitasking. Therefore, organizing instructional interventions according to an empirically questionable multitasking superiority seems problematic.

Abstract

Adaptive learning systems need large pools of examples for practice—thousands of items that need to be organized into hundreds of knowledge components within a domain model. Domain modeling and closely related student modeling are intensively studied in research studies. However, there is a gap between research studies and practical issues faced by developers of scalable educational technologies. The aim of this paper is to bridge this gap by connecting techniques and notions used in research papers to practical problems in development. We put specific emphasis on scalability—on techniques that enable relatively cheap and fast development of adaptive learning systems. We summarize conceptual questions in domain modeling, provide an overview of approaches in the research literature, and discuss insights based on the development and analysis of a widely used system. We conclude with recommendations for both developers and researchers in the area of adaptive learning systems.

Abstract

This study aimed to classify latent profiles of Korean undergraduates’ academic emotions in an e-learning environment, and to examine the effects of instructional variables on these profiles as well as the differences in their learning outcomes. A survey was conducted among 777 students who participated in online courses offered by a Korean university. Latent profile analysis revealed four types of emotional profiles: a moderate type (MT); a positive type (PT); a negative type (NT); and an ambivalent type (AT). MT comprised 72.5% of the total number of participants and showed medium levels of both positive emotions (PE) and negative emotions (NE). PT comprised 13.1% of the participants and showed high levels of PE and low levels of NE. NT comprised 10.2% of the participants and showed low levels of PE and high levels of NE. AT comprised 4.2% of the participants and both showed high levels of both PE and NE. Further analysis showed that the quality of instructional content, interaction, the system, and evaluation all proved to be predictors of emotional profiles. Moreover, they indicated differences in perceived achievement and in learner satisfaction. Based on these results, this study provides a discussion and suggestions for further studies.

Abstract

This mixed methods study is aimed to examine the feasibility of integrating mathematical problem solving with architectural design via a 3D epistemic simulation game to promote active mathematics learning for middle-school students. The experimental-control pretest/posttest group design was adopted to examine whether the experience of interacting with an architecture simulation game would improve students’ math knowledge for and performance of problem solving. Data were collected from 61 6th graders via both quantitative and qualitative methods, including math problem-solving and mental rotation tests, video- and screen-capture of game play behaviors, observation, as well as game logs. The study results indicated that the gaming group performed significantly better than the non-gaming control group in the math context problem solving test. The infield observation and participants’ gaming behavior analysis suggested that the learning and practice of mathematical problem solving during gaming is a cognizant and planned endeavor framed by carefully designed game actions and objects.

Abstract

In order to fully realise the potential of online and blended learning (OBL), teacher professional development (TPD) strategies on how to teach in an online or blended learning environment are needed. While many studies examine the effects of TPD strategies, fewer studies target the specific important components of these strategies. This study addresses that gap by conducting a systematic review of qualitative data consisting of 15 articles on TPD that targets OBL. Using a meta-aggregative approach, six different synthesised findings were identified and integrated into a visual framework of the key components of TPD for OBL. These synthesised findings are the base for the action recommendations which present specific and contextualised suggestions. Taken together, the findings can inform in-service teachers and trainers, together with further research and development efforts that are concerned with TPD for OBL.

Abstract

In this critical literature review, we seek to understand why multidimensional, psychological measures of human emotion that have been popular in the study of emotion and learning to date, may not yield the statistical power or construct validity necessary to consistently explain or predict human learning. We compare competing theories and conclude that educational studies tend towards use of multi-dimensional models of human emotions which, while useful in educational psychology and therapeutic practice, suffer from psychometric flaws and generate lower power when used as empirical research constructs compared with the “basic emotion” models and their derivatives popular in the neurobiological, cognitive, and social sciences. Based on our review, we conclude that more extensive use of physiological measures and analysis of spontaneous emotion language, both rooted in the basic emotions tradition rather than continued psychological measurement of multi-dimensional emotions, may yield more consistent and significant results and reduce education researchers’ reliance on self-report measures. Findings from the review may advance the selection of operational definitions and formulation of research questions for new empirical studies of the intersections between emotion and learning.

Abstract

Because a large number of Chinese characters are commonly used in both Japanese and Chinese, Chinese-speaking learners of Japanese as a second language (JSL) find it more challenging to learn Japanese functional expressions than to learn other Japanese vocabulary. To address this challenge, we have developed Jastudy, a computer-assisted language learning (CALL) system designed specifically for Chinese-speaking learners studying Japanese functional expressions. Given a Japanese sentence as an input, the system automatically detects Japanese functional expressions using a character-based bidirectional long short-term memory with a conditional random field (BiLSTM-CRF) model. The sentence is then segmented and the parts of speech (POS) are tagged (word segmentation and POS tagging) by a Japanese morphological analyzer, MeCab (http://taku910.github.io/mecab/), trained using a CRF model. In addition, the system provides JSL learners with appropriate example sentences that illustrate Japanese functional expressions. The system uses a ranking system, which gives easier sentences a higher rank, when selecting example sentences. A support vector machine for ranking (SVMRank) algorithm estimates the readability of example sentences, using Japanese-Chinese common words as an important feature. A k-means clustering algorithm is used to cluster example sentences that contain functional expressions with the same meanings, based on part-of-speech, conjugation form, and semantic attributes. Finally, to evaluate the usefulness of the system, we have conducted experiments and reported on a preliminary user study involving Chinese-speaking JSL learners.

Abstract

In this paper, we describe an effort to develop and evaluate an innovative formative assessment to gather evidence about middle school students’ argumentation skills. Specifically, this game-enhanced scenario-based assessment (Seaball—Semester at Sea) includes a series of argumentative reasoning activities in the context of an extended scenario wherein students debate the issue of whether junk food should be sold to students. These activities were designed around argumentation learning progressions (i.e., hypotheses about the qualitative shifts that occur as students achieve higher levels of sophistication in argumentation) which serve as a framework to determine the targeted skills, levels and activity sequences. Performance feedback is also provided in the assessment. We conducted a pilot study, aimed at examining student performance and the validity of the tasks as a measure of argumentation skills. More than 100 middle school students completed this assessment and additional external measures of argumentation in a pre/post design. Descriptive statistics of student performance in the activities, analyses of item difficulty, and correlations are reported. Results indicated that students’ total scores were significantly correlated with external measures of argumentation skills, and with students’ state reading and writing test scores. In addition, students achieved higher average scores in a post-test of argumentation skills after having completed the Seaball activities. Finally, explanatory feedback about students’ task performance was found to be beneficial to those who were “Below” or “Approaching” proficient on the state reading and writing test. We conclude with implications for assessment design and instruction in argumentation.

Abstract

The field of adaptive e-learning is continuously developing. More research is being conducted in this area as adaptive e-learning aims to provide learners with adaptive learning paths and content, according to their individual characteristics and needs, which makes e-learning more efficient and effective. The learner model, which is a representation of different learner’s characteristics, plays a key role in this adaptation. This paper presents a systematic literature review about learner modelling during the last 5 years, describing the different modelled characteristics and the adopted modelling techniques and modeling types: automatic modeling and collaborative modeling. 107 publications were selected and analyzed, and six categories of the modelled characteristics were identified. This literature review contributes to the identification of the learners’ individual traits and presents the most used modelling techniques for each of them. It also identifies the latest research trends of Learner Modeling and generates future research directions in this field.

Abstract

The flipped classroom model is a form of blended learning in which delivery of content occurs with online materials, and face-to-face meetings are used for teacher-guided practice. It is important that teachers stay up to date with the activities students engage in, which may be accomplished with the help of learning analytics (LA). This study investigated university teachers’ perceptions of whether weekly LA reports that summarized student activities supported their teaching activities (n = 7). The teachers reported using the LA reports for diagnosing and intervening during student activities, and that the reports encouraged them to start interaction with students. Teachers did sometimes find it difficult to connect the information from the LA reports to concrete interventions, which was partly dependent on the level of the teacher’s experience. LA reports might support teachers further by not only offering information, but also by suggesting interventions.

In the abstract, the second “FG” in the sentence below should be “SG”:

The sample was classified into three cultural groups: 203 first-generation immigrant students (FG), 354 second-generation immigrant students (FG), and 521 non-immigrant student (Native).

Thus, the original sentence should be corrected as follows:

The sample was classified into three cultural groups: 203 first-generation immigrant students (FG), 354 second-generation immigrant students (SG), and 521 non-immigrant student (Native).

Abstract

As one of the most widely adopted mobile and social media applications, Tencent WeChat ® (‘WeChat’) has been increasingly used in education at all levels in Asia, and in China in particular. However, only a small number of studies have been conducted to explore educational affordances of WeChat. In this paper, these affordances are defined as opportunities for an educational activity that are determined and supported by perceived and actual features of a technology tool or an environment (Gibson in The ecological approach to visual perception, Houghton Mifflin, Boston, 1979; Norman in The psychology of everyday things, Basic Books, New York, 1988; Sanders in Ecol Psychol 9(1):97–112, 1997). The authors conducted a review of 21 studies out of a pool of 1984 identified publications on the topic to examine existing practices, empirical studies and recommendations for the uses of WeChat, and with the over-reaching aim of articulating a framework for the adoption of educational affordances of mobile social media. Such framework will serve practice as well as research on educational uses of mobile social media and help extend theory of affordances in this domain. A total of seven categories of educational affordances of WeChat were explicated and included in this framework: Resources Sharing, Authentic Learning, Collaboration, Community Building, Motivating Environment, Evaluation and Feedback, and Administration for Learning. Guidelines for the adoption of this framework are developed, and suggestions for future research are proposed.

Abstract

This paper describes the development of a software program that supports argumentative reading and writing, especially for novice students. The software helps readers create a graphic organizer from the text as a knowledge map while they are reading and use their prior knowledge to build their own opinion as new information while they think about writing their essays. Readers using this software can read a text, underline important words or sentences, pick up and dynamically cite the underlined portions of the text onto a knowledge map as quotation nodes, illustrate a knowledge map by linking the nodes, and later write their opinion as an essay while viewing the knowledge map; thus, the software bridges argumentative reading and writing. Sixty-three freshman and sophomore students with no prior argumentative reading and writing education participated in a design case study to evaluate the software in classrooms. Thirty-four students were assigned to a class in which each student developed a knowledge map after underlining and/or highlighting a text with the software, while twenty-nine students were assigned to a class in which they simply wrote their essays after underlining and/or highlighting the text without creating knowledge maps. After receiving an instruction regarding a simplified Toulmin’s model followed by instructions for the software usage in argumentative reading and writing along with reading one training text, the students read the target text and developed their essays. The results revealed that students who drew a knowledge map based on the underlining and/or highlighting of the target text developed more argumentative essays than those who did not draw maps. Further analyses revealed that developing knowledge maps fostered an ability to capture the target text’s argument, and linking students’ ideas to the text’s argument directly on the knowledge map helped students develop more constructive essays. Accordingly, we discussed additional necessary scaffolds, such as automatic argument detection and collaborative learning functions, for improving the students’ use of appropriate reading and writing strategies.

Abstract

In an effort to create meaningful user experiences, instructional designers participate in continuous projection and reflection during design. Empathic design draws on instructional designers’ sensitivity toward their learners as a reference for design. Empathic forecasting, or predictions about an emotional reaction to future events, is an important influence on design in general and may be particularly meaningful for empathic design. This exploratory mixed-methods study examined how instructional designers’ imagined the cognitive and emotional learner experience as they designed a collaboration-based interactive case study to promote interaction and collaboration among physicians, radiobiologists, and radiation physicists. We employed a protocol analysis methodology to document the verbal exchanges of members of this design team during collaborative meetings. Online surveys that included scale-based ratings and short open-ended questions assessed learners’ perceptions of their instructional experience. Findings indicate that instructional designers visualized learner interaction with the Virtual Hospital, and emoted learner feelings with the activity while engaging in design. User results indicate that the outcome the instructional designers envisioned for the user experience aligned with user perceptions of their experiences during the activity.

Abstract

By collecting longitudinal learner and learning data from a range of resources, predictive learning analytics (PLA) are used to identify learners who may not complete a course, typically described as being at risk. Mixed effects are observed as to how teachers perceive, use, and interpret PLA data, necessitating further research in this direction. The aim of this study is to evaluate whether providing teachers in a distance learning higher education institution with PLA data predicts students’ performance and empowers teachers to identify and assist students at risk. Using principles of Technology Acceptance and Academic Resistance models, a university-wide, multi-methods study with 59 teachers, nine courses, and 1325 students revealed that teachers can positively affect students’ performance when engaged with PLA. Follow-up semi-structured interviews illuminated teachers’ actual uses of the predictive data and revealed its impact on teaching practices and intervention strategies to support students at risk.

Abstract

Lectures in higher education often address audiences that consist of over one hundred students. In this setting, it is arguably difficult to take into account individual interests of each participant. This may result in low motivation, decreased learning outcomes as well as an overall low effectiveness of lectures. Self-determination theory suggests that perceived autonomy increases intrinsic motivation, which may in turn improve learning outcomes. We therefore propose to foster perceived autonomy among students by introducing elected elements (e.g., practical examples and topics) that students can vote for with an audience response system. To investigate this instructional approach, we conducted a quasi-experimental field study with two groups of participants: One group was given the choice over some content of the lectures while the other group attended an identical course without choice. Results show that providing the choice over elected elements leads to an increase in perceived influence on the course. Students who reported high perceived influence also experienced high intrinsic motivation. Regarding learning outcomes, intrinsically motivated students reported high perceived learning gains, yet there was no association with test performance. Based on these findings, we derive several avenues for future research regarding the use of elected elements in large-scale lectures.

Abstract

The design of multimedia elements used in video for online courses can increase student perceptions of their instructor’s credibility and immediacy. Credibility is the learner’s perception of the subject matter expertise of the instructor, while immediacy is the learner’s perception of the instructor’s ability to communicate and reduce physiological distance. This experiment randomly assigned research participants (N = 211) into one of five independent treatment groups, each group viewed a different design based on the same subject matter, instructor video, audio narration, and presentation slides. These presentation designs included an instructor-only, slides-only, video-switching, dual-windows, and a superimposed-slides multimedia design variation. A series of 5 × 1 Analyses of variances and Tukey post hoc calculations were conducted to test for statistically significant differences between groups. The results suggest that a balance can be established between instructor credibility and immediacy by showing both the instructor and instructional content during online classes.

Presents corrections to author names in the paper, “Jutge.org: Characteristics and experiences,” IEEE Trans. Learn. Technol., vol. 11, no. 3, pp. 321–333, Jul.–Sep. 2018.

Most educational institutions adopted the hybrid teaching mode through learning management systems. The logging data/clickstream could describe learners' online behavior. Many researchers have used them to predict students' performance, which has led to a diverse set of findings, but how to use insights from captured data to enhance learning engagement is an open question. Furthermore, identifying students at risk of failure is only the first step in truly addressing this issue. It is important to create actionable predictive model in the real-world contexts to design interventions. In this paper, we first extracted features from students' learning activities and study habits to predict students' performance in the Kung Fu style competency education. Then, we proposed a TrAdaBoost-based transfer learning model, which was pretrained using the data of the former course iteration and applied to the current course iteration. Our results showed that the generalization ability of the prediction model across the teaching iterations is high, and the model can achieve relatively high precision even when the new data are not sufficient to train a model alone. This work helped in timely intervention toward the at-risk students. In addition, two intervention experiments with split-test were conducted separately in Fall 2017 and Summer 2018. The statistical tests showed that both behavior-based reminding intervention and error-related recommending intervention that based on early prediction played a positive role in improving the blended learning engagement.

This paper presents the work done to support student dropout risk prevention in a real online e-learning environment: A Spanish distance university with thousands of undergraduate students. The main goal is to prevent students from abandoning the university by means of retention actions focused on the most at-risk students, trying to maximize the effectiveness of institutional efforts in this direction. With this purpose, we generated predictive models based on the C5.0 algorithm using data from more than 11,000 students collected along five years. Then, we developed SPA (Sistema de Predicciæn de Abandono, dropout prediction system in Spanish), an early warning system that uses these models to generate static early dropout-risk predictions and dynamic periodically updated ones. It also supports the recording of the resulting retention-oriented interventions for further analysis. SPA is in production since 2017 and is currently in its fourth semester of continuous use. It has calculated more than 117,000 risk scores to predict the dropout risk of more than 5700 students. About 13,000 retention actions have been recorded. The white-box predictive models used in production provided reasonably good results, very close to those obtained in the laboratory. On the way from research to production, we faced several challenges that needed to be effectively addressed in order to be successful. In this paper, we share the challenges faced and the lessons learnt during this process. We hope this helps those who wish to cross the road from predictive modeling with potential value to the exploitation of complete dropout prevention systems that provide sustained value in real production scenarios.

Identifying at-risk students as soon as possible is a challenge in educational institutions. Decreasing the time lag between identification and real at-risk state may significantly reduce the risk of failure or disengage. In small courses, their identification is relatively easy, but it is impractical on larger ones. Current Learning Management Systems store a large amount of data that could help to generate predictive models to early identification of students in online and blended learning. The contribution of this paper is twofold: First, a new adaptive predictive model is presented based only on students' grades specifically trained for each course. A deep analysis is performed in the whole institution to evaluate its performance accuracy. Second, an early warning system is developed, focusing on dashboards visualization for stakeholders (i.e., students and teachers) and an early feedback prediction system to intervene in the case of at-risk identification. The early warning system has been evaluated in a case study on a first-year undergraduate course in computer science. We show the accuracy of the correct identification of at-risk students, the students' appraisal, and the most common factors that lead to at-risk level.

Developing tools to support students and learning in a traditional or online setting is a significant task in today's educational environment. The initial steps toward enabling such technologies using machine learning techniques focused on predicting the student's performance in terms of the achieved grades. However, these approaches do not perform as well in predicting poor-performing students. The objective of our work is twofold. First, in order to overcome this limitation, we explore if poorly performing students can be more accurately predicted by formulating the problem as binary classification, based on data provided before the start of the semester. Second, in order to gain insights as to which are the factors that can lead to poor performance, we engineered a number of human-interpretable features that quantify these factors. These features were derived from the students' grades from the University of Minnesota, an undergraduate public institution. Based on these features, we perform a study to identify different student groups of interest, while at the same time, identify their importance. As the resulting models provide us with different subsets of correct predictions, their combination can boost the overall performance.

In the European academic systems, the public funding to single universities depends on many factors, which are periodically evaluated. One of such factors is the rate of success, that is, the rate of students that do complete their course of study. At many levels, therefore, there is an increasing interest in being able to predict the risk that a student will abandon the studies, so that (specific, personal) corrective actions may be designed. In this paper, we propose an innovative temporal optimization model that is able to identify the earliest moment in a student's career in which a reliable prediction can be made concerning his/her risk of dropping out from the course of studies. Unlike most available models, our solution can be based on the academic behavior alone, and our evidence suggests that by ignoring classically used attributes such as the gender or the results of pre-academic studies one obtains more accurate, and less biased, models. We tested our system on real data from the three-year degree in computer science offered by the University of Ferrara (Italy).

Educational data mining has gained a lot of attention among scientists in recent years and constitutes an efficient tool for unraveling the concealed knowledge in educational data. Recently, semisupervised learning methods have been gradually implemented in the educational process demonstrating their usability and effectiveness. Cotraining is a representative semisupervised method aiming to exploit both labeled and unlabeled examples, provided that each example is described by two features views. Nevertheless, it is yet to be used in various scientific fields, among which the educational field as well, since the assumption about the existence of two feature views cannot be easily put into practice. Within this context, the main purpose of this study is to evaluate the efficiency of a proposed cotraining method for early prognosis of undergraduate students' performance in the final examinations of a distance course based on a plethora of attributes which are naturally divided into two distinct views, since they are originated from different sources. More specifically, the first view consists of attributes regarding students' characteristics and academic achievements which are manually filled out by their tutors, whereas the second one consists of attributes tracking students' online activity in the course learning management system and which are automatically recorded by the system. The experimental results demonstrate the superiority of the proposed cotraining method as opposed to state-of-the-art semisupervised and supervised methods.

Early warning systems have been progressively implemented in higher education institutions to predict student performance. However, they usually fail at effectively integrating the many information sources available at universities to make more accurate and timely predictions, they often lack decision-making reasoning to motivate the reasons behind the predictions, and they are generally biased toward the general student body, ignoring the idiosyncrasies of underrepresented student populations (determined by socio-demographic factors such as race, gender, residency, or status as a freshmen, transfer, adult, or first-generation students) that traditionally have greater difficulties and performance gaps. This paper presents a multiview early warning system built with comprehensible Genetic Programming classification rules adapted to specifically target underrepresented and underperforming student populations. The system integrates many student information repositories using multiview learning to improve the accuracy and timing of the predictions. Three interfaces have been developed to provide personalized and aggregated comprehensible feedback to students, instructors, and staff to facilitate early intervention and student support. Experimental results, validated with statistical analysis, indicate that this multiview learning approach outperforms traditional classifiers. Learning outcomes will help instructors and policy-makers to deploy strategies to increase retention and improve academics.

Blended courses that mix in-person instruction with online platforms are increasingly common in secondary education. These platforms record a rich amount of data on students' study habits and social interactions. Prior research has shown that these metrics are correlated with students performance in face-to-face classes. However, predictive models for blended courses are still limited and have not yet succeeded at early prediction or cross-class predictions, even for repeated offerings of the same course. In this paper, we use data from two offerings of two different undergraduate courses to train and evaluate predictive models of student performance based on persistent student characteristics including study habits and social interactions. We analyze the performance of these models on the same offering, on different offerings of the same course, and across courses to see how well they generalize. We also evaluate the models on different segments of the courses to determine how early reliable predictions can be made. This paper tells us in part how much data is required to make robust predictions and how cross-class data may be used, or not, to boost model performance. The results of this study will help us better understand how similar the study habits, social activities, and the teamwork styles are across semesters for students in each performance category. These trained models also provide an avenue to improve our existing support platforms to better support struggling students early in the semester with the goal of providing timely intervention.

A significant amount of research effort has been put into finding variables that can identify students at risk based on activity records available in learning management systems (LMS). These variables often depend on the context, for example, the course structure, how the activities are assessed or whether the course is entirely online or a blended course. To the best of our knowledge, a predictive model that can generalize well to many different types of courses using data available in the LMS does not currently exist in the learning analytics literature. In this study, early prediction of students at risk is tackled by training a number of neural networks to predict which students would likely submit their assignments on time based on their activity up to two days before assignments' due dates. Five different datasets that cover a total of 78 722 student enrolments in 5487 courses have been used in this study. In order to improve how well the neural networks generalize, our networks can perform different forms of feature engineering using course peers data. The different architectures of these networks have been compared to find the one with more predictive power. To validate the models trained from the networks, both new datasets and unseen examples extracted from the same datasets have been used for training. Our research show that adding contextual information results in better prediction accuracies and F1 scores. Our networks are able to give predictions with accuracies in the 67.46-81.63% range and F1 scores in the 71.30-83.09% range.

The increased usage of computer-based learning platforms and online tools in classrooms presents new opportunities to not only study the underlying constructs involved in the learning process, but also use this information to identify and aid struggling students. Many learning platforms, particularly those driving or supplementing instruction, are only able to provide aid to students who interact with the system. With this in mind, student persistence emerges as a prominent learning construct contributing to students success when learning new material. Conversely, high persistence is not always productive for students, where additional practice does not help the student move toward a state of mastery of the material. In this paper, we apply a transfer learning methodology using deep learning and traditional modeling techniques to study high and low representations of unproductive persistence. We focus on two prominent problems in the fields of educational data mining and learner analytics representing low persistence, characterized as student “stopout,” and unproductive high persistence, operationalized through student “wheel spinning,” in an effort to better understand the relationship between these measures of unproductive persistence (i.e., stopout and wheel spinning) and develop early detectors of these behaviors. We find that models developed to detect each within and across-assignment stopout and wheel spinning are able to learn sets of features that generalize to predict the other. We further observe how these models perform at each learning opportunity within student assignments to identify when interventions may be deployed to best aid students who are likely to exhibit unproductive persistence.

Performance prediction is a leading topic in learning analytics research due to its potential to impact all tiers of education. This study proposes a novel predictive modeling method to address the research gaps in existing performance prediction research. The gaps addressed include: the lack of existing research focus on performance prediction rather than identifying key performance factors; the lack of common predictors identified for both K-12 and higher education environments; and the misplaced focus on absolute engagement levels rather than relative engagement levels. Two datasets, one from higher education and the other from a K-12 online school with 13 368 students in more than 300 courses, were applied using the predictive modeling technique. The results showed the newly suggested approach had higher overall accuracy and sensitivity rates than the traditional approach. In addition, two generalizable predictors were identified from instruction-intensive and discussion-intensive courses.

The papers in this special section focus on early prediction and the support of learning performance. Predicting student's learning performance in traditional face-to-face learning, online learning (LMS, MOOCs, etc.), and blended learning is a challenging but essential task in education [1]. On the one hand, it has become a difficult challenge due to the high number of factors that can influence a student’s final status. On the other hand, it is a critical issue in education because it concerns many students of all levels (primary education, secondary education, and tertiary or higher education) and institutions over the entire world. Moreover, also, an increase in the number of low performing students can cause a lower graduation rate, an inferior institution reputation in the eyes of all involved, and it usually results in overall financial loss. The task of predicting students’ performance is one of the oldest and most studied tasks in Educational Data Mining (EDM) and Learning Analytics (LA), and a wide range of classification and regression approaches have been successfully applied.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Presents the table of contents for this issue of the publication.

Abstract

This study focuses on visual creativity and how it can be supported with computer technologies and thereby be used to support learning and instruction. However, studies related to computer-enabled visual creativity have not been frequently explored. As such, the current research proposes a model consisting of four major factors: (a) computer-aided visual art self-efficacy, (b) computer self-efficacy, (c) general creative self-efficacy, and (d) visual creativity. The aim is to explore the causal relationships among these factors so that they can then be used to support creativity, especially in the context of learning and instruction. To test the proposed model, this study firstly collected a total of 736 responses from an American public university to construct a scale using exploratory factor analyses and confirmatory factor analyses for three factors: (a) computer self-efficacy, (b) computer-aided visual art self-efficacy, and (c) general creative self-efficacy. Later, 164 responses were collected to analyze those hypothesized predictors of visual creativity and their relationships using structural equation modeling with Mplus. The results of the study indicate that computer self-efficacy was a significant predictor of computer-aided visual art self-efficacy, which in turn was a significant predictor of general creative self-efficacy. General creative self-efficacy, in turn, was a significant predictor of visual creativity. Finally, the study yielded a significant indirect effect of computer-aided visual art self-efficacy on visual creativity as mediated by general creative self-efficacy. Implications for learning and instruction are discussed as well as future studies to further research to develop relevant models of visual creativity in support of learning.

Abstract

Students’ grasp of the non-mathematical language in a mathematics story problem—such as vocabulary and syntax—may have an important effect on their problem-solving, and this may be particularly true for students with weaker language skills. However, little experimental research has examined which individual language features influence students’ performance while solving problems—much research has been correlational or has combined language features together. In the present study, we manipulated six different language features of algebra story problems—number of sentences, pronouns, word concreteness, word hypernymy, consistency of sentences, and problem topic—and examined how systematically varying readability demands impacts student performance. We examined both accuracy and response time measures, using an assignment for learning linear functions in the ASSISTments online problem-solving environment. We found little evidence that individual language features have a considerable effect on mathematics word problem solving performance for a general population of students. However, sentence consistency reduced response time and problems about motion or travel had shorter response times than problems about business or work. In addition, it appears students may benefit or be harmed by language modifications depending on their familiarity with ASSISTments. Implications for the role of language in math word problems are discussed.

Abstract

Collaborative learning is a widely used instructional technique, but factors determining its effectiveness still are unclear. Cognitive load theory was used to examine the effects of prior collaborative experience and density of distribution of information amongst learners on short-term retention and delayed retention tests, as well as cognitive efficiency of collaborative learning and its outcomes. Data obtained with 240 secondary school students showed that groups with experience in collaboration outperformed and were more cognitively efficient than inexperienced groups, and low information density increased performance during the learning process. Also, when tasks required processing high information density, experienced groups were more cognitively efficient than inexperienced groups. For tasks with low information density no difference was found. These results provide instructional implications for designing effective collaborative learning environments.

Abstract

This study investigated the effects of creative-map instructional strategies on learning performance, learning motivation, and creativity in a junior high school geography class. A quasi-experimental approach was used to assess the treatment effects among 79 ninth graders, utilizing qualitative data including students’ feedback, and four quantitative instruments: filling-in map quizzes, geography term exams, the Learning Motivation Scale for Primary and Junior High School Students, and the Newly Revised Creative Thinking Tests. Repeated-measures ANCOVA were performed to analyze the correlation coefficients between the experimental and control groups, and indicated that the former group performed better than the latter in learning performance, motivation, and creativity after the intervention. Thus, it can be concluded that creative-map mnemonic strategies can have a positive impact on the learning and retention of place names and locations. Implications for further research and practice are also discussed.

Abstract

Engaging in engineering tasks can help students learn science concepts. However, many engineering tasks lead students to focus more on the success of their construction than on learning science content, which can hurt students’ ability to learn and transfer scientific principles from them. Two empirical studies investigate how content-focused learning goals and contrasting cases affect how students learn and transfer science concepts from engineering activities. High school students were given an engineering challenge, which involved understanding and applying center of mass concepts. In Study 1, 86 students were divided into four conditions where both goals (content learning vs. outcome) and instructional scaffolds (contrasting cases vs. no cases) were manipulated during the engineering task. Students with both content-focused learning goals and contrasting cases were better able to transfer scientific principles to a new task. Meanwhile, regardless of condition, students who noticed the deep structure in the cases demonstrated greater learning. A second study tried to replicate the goal manipulation findings, while addressing some limitations of Study 1. In Study 2, 78 students received the same engineering task with contrasting cases, while half the students received a learning goal, and half received an outcome goal. Students who were given content-focused learning goals valued science learning resources more and were better able to transfer scientific principles to novel situations on a test. Across conditions, the more students valued resources, the more they learned, and students who noticed the deep structure transferred more. This research underscores the importance of content-focused learning goals for supporting transfer of scientific principles from engineering tasks, when students have access to adequate instructional scaffolds.

Abstract

The goal of this study was to investigate cognitive style (the visualizer–verbalizer dimension) and cognitive ability (spatial and verbal abilities) in terms of corresponding resource use behavior. The study further examined the potential link between cognitive style and cognitive ability based on observable behavior. A total of 67 university students participated in the study by completing an online survey containing a series of questionnaires, tests, and tasks, which assessed their cognitive style, cognitive ability, and resource use behavior. Multinomial logistic regression analyses revealed that cognitive style in general predicts resource use behavior. The findings also showed that spatial ability, particularly lower spatial ability, predicts resource use behavior. This study thus contributes to the literature with theory-based, empirical evidence that cognitive ability is reflected in cognitive style. This study further provides information needed to better understand the interplay between individuals’ cognitive style and cognitive ability and how these may be addressed in the design and implementation of learning environments.

Abstract

K-12 classroom settings are not yet incorporating emerging technologies such as ubiquitous computing, augmented reality, nor even touch surfaces, despite the significant impact that such media have made in many other aspects of our lives. Unfortunately, classroom environments have not generally evolved to support students in the new modes of collaboration, idea sharing, and inquiry that characterize many of our research-based innovations. Responding to this challenge, our research was conducted by a multi-disciplinary design team including educational researchers, a high school physics teacher, and technology designers. We embarked on a series of design-based research projects to investigate a smart classroom infrastructure that scaffolds students and teachers in new forms of collaboration and inquiry, including a substantive role for large projected displays and small touch surfaces, as well as a dependency on students’ physical location within the room. This paper describes our designs, including: (1) the role of large displays for communicating aggregate and ambient information, (2) the role of real-time communication between students, (3) the application of intelligent software agents to enact real-time pedagogical logic, (4) support for learning across contexts, and (5) orchestration of inquiry roles, materials and environments. These designs are particularly relevant for the Learning Sciences community, as they offer insight into how the orchestrated classroom can support new forms of collaborative, cooperative and collective inquiry. One important outcome of this work is a set of design principles for supporting smart classroom research.

Abstract

We compared performances on a learning task in which students (N = 81) viewed a pedagogical multimedia document without (control group) or with a readymade graphic organizer (readymade group) with performances on an active learning task where students self-generated a graphic organizer either totally (total self-generated group) or partially (partial self-generated group) while learning from the same multimedia document. According to the generative hypothesis, asking students to actively engage in the construction of a graphic organizer enhances their learning, owing to the generative processes (selection, organization, integration) required to perform the task. However, according to the cognitive load hypothesis, generating a graphic organizer can hinder students’ learning, owing to the extraneous processing elicited by the task. It can nonetheless be assumed that if scaffolding is provided to students in the shape of an empty graphic organizer to fill in, these negative effects can be avoided. Results confirmed the beneficial effect of providing a graphic organizer on students’ retention of the elements contained in the multimedia document (macrostructure information, hierarchical relations). Evidence in favor of the cognitive load hypothesis and against the generative hypothesis was found, as students in the total self-generated group performed more poorly on the retention and transfer tests than those in the readymade group. This negative effect on learning ceased to be observed when scaffolding was provided to students in the partial self-generated group, although they still spent more time on the document than those in the readymade group. Overall, we failed to observe any beneficial effect of generation on learning.

Abstract

This study examined whether kindergarten children’s psychological needs satisfaction would mediate the relationships between parental scaffolding and children’s use of self-regulated learning (SRL) strategic behaviours. One hundred and thirty Chinese kindergarten children and their parents participated in the study. Parental scaffolding and children’s SRL strategic behaviours were respectively observed in parent–child interaction tasks and child-alone tasks. Drawing on self-determination theory (SDT), children’s satisfaction of three basic needs for competence, autonomy, and relatedness was assessed using both behavioural observation and self-report measures. Among the three aspects of observed needs satisfaction, children’s observed satisfaction of the need for competence was particularly important, mediating all the relationships between three aspects of parental scaffolding and three aspects of children’s SRL strategic behaviours. Children’s perceived needs satisfaction, despite having some correlations with parental scaffolding and children’s SRL, did not mediate any relationships between parental scaffolding and children’s SRL strategic behaviours, which further revealed limitations associated with using self-report measures with young children. The study provides preliminary evidence of the mediating role of psychological needs satisfaction in the relationships between parental scaffolding and children’s SRL in problem-solving situations.

Abstract

Previous studies have examined the effects of service-learning on student outcomes, but the dynamics and the mechanism of student development have received little attention. The present study aims to investigate how students construct their understanding of course content through service-learning, as well as the role of varied experiences. Eighty-four students were randomly assigned to two different conditions: the low-varied experiences condition (n = 36), in which students served the same child with autism throughout the programme, and the highly-varied experiences condition (n = 48), in which students served two children with autism successively. A total of 483 reflective journals written by students in a 6-week timeframe were analysed. The results indicated that students gained benefits from service-learning in terms of knowledge construction, and the overall change in students’ knowledge construction fluctuated throughout the service-learning process. In addition, students in the highly-varied experiences condition also demonstrated some differences in knowledge construction changes, indicating that varied service experiences might interfere with students’ knowledge construction at the turning point of task changing. The implications for service-learning and instruction are also discussed.

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Volume 27, Issue 8, December 2019, Page 991-994
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