Epistemological beliefs and epistemic strategies in self-regulated learning

How do epistemological attitudes and beliefs influence learning from text? We conceptualize epistemological attitudes and beliefs as components of metacognitive knowledge. As such, they serve an important function in regulating the use of epistemic strategies such as knowledge-based validation of information and checking arguments for internal consistency. We report results from two studies that investigated the effects epistemological attitudes and beliefs on the use of epistemic strategies in academic learning and the motivational states that mediate these effects. Study 1 (N = 289) tested a mediation model with epistemological attitudes (separate vs. connected knowing) and textual characteristics as distal predictors, and learning goals (learning factual knowledge vs. developing an own standpoint) as mediator variables. Separate knowing had large indirect effects on the use of epistemic strategies via the goal to develop an own point of view. In addition, learners adapted their learning goals and epistemic strategies depending on objective characteristics and the perceived familiarity of the texts they read. In Study 2 (N = 124), epistemological beliefs concerning the uncertainty of knowledge increased the use of epistemic strategies only when extrinsic study motivation was low. A mediated moderation model established this effect to be mediated by specific epistemic curiosity. These results illuminate the mechanisms of how epistemological attitudes and beliefs affect self-regulated learning. In contrast to other types of learning strategies, the use of epistemic strategies seems to be strongly and consistently linked to epistemological attitudes and beliefs.     

Coordinated Strategies to Help the Whole Child: Examining the Contributions of Full-Service Community Schools

Full-service community schools are designed to increase students, and families' access to comprehensive and coordinated supports, services, and programs such as medical care, food aid, and enrichment activities. Despite widespread support, the research base documenting the efficacy of community schools is still emerging. Analyzing longitudinal data from 6 low-income, predominantly Latino community schools, this case study investigated whether students' participation in community-school programming influenced their attendance and achievement. Multilevel models indicated that youth who participated in extended learning programs or whose families were involved in family engagement opportunities exhibited greater attendance rates and achievement in math and English language arts, when compared to their nonparticipating peers. Findings provide promising evidence on the value of a comprehensive and integrated system of school supports that address the myriad needs of vulnerable student populations.     

More is not always better: Effects of collaboration breadth and depth on radical and incremental innovation performance at the project level

This study investigates the effect of project-level collaboration breadth (i.e., the number of collaboration partner types) and collaboration depth (i.e., the intensity of the interactions with these partners) on the incremental and radical innovation performance of innovation projects. The econometric analyses, based on a Community Innovation Study sample of 218 innovation projects conducted in German manufacturing companies, reveal inverted U-shaped relationships between collaboration breadth and radical innovation performance and between collaboration depth and incremental innovation performance. These curvilinear effects speak to the high sensitivity of innovation projects to the extent of collaborative activity, which implies that practitioners should exert caution in managing collaborative innovation projects. This study contributes to the understanding of project-level open innovation and to the overall understanding of the performance effects of innovation collaboration, which, following recent assertions in the literature, could be flawed by analyses conducted at the organizational level.     

Das MINERVA-Projekt: Datenbankselektion für Peer-to-Peer-Websuche

In diesem Artikel wird MINERVA pr?sentiert, eine prototypische Implementierung einer verteilten Suchmaschine basierend auf einer Peer-to-Peer (P2P)-Architektur. MINERVA setzt auf die in der P2P-Welt verbreitete Technik verteilter Hash-Tabellen auf und benutzt diese zum Aufbau eines verteilten Verzeichnisses. Peers in unserem Ansatz entsprechen v?llig autonomen Benutzern mit ihren lokalen Suchm"oglichkeiten, die bereit sind, ihr lokales Wissen und ihre lokalen Suchm?glichkeiten im Rahmen einer Kollaboration zur Verfügung zu stellen. Wir formalisieren unsere Systemarchitektur und beschreiben das zentrale Problem einer effizienten Suche nach vielversprechenden Peers für eine konkrete Anfrage innerhalb des Verbundes. Wir greifen dabei auf existierende Methoden zurück and passen diese an unseren Systemkontext an. Wir pr?sentieren Experimente auf realen Daten, die verschiedene dieser Ans?tze vergleichen. Diese Experimente zeigen, dass die Qualit?t der Ans?tze variiert und untermauern damit die Wichtigkeit und den Einfluss einer leistungsstarken Methode zur Auswahl guter Datenbanken. Unsere Experimente deuten an, dass eine geringe Anzahl sorgf?ltig ausgew?hlter Datenbanken typischerweise bereits einen Gro?teil aller relevanten Ergebnisse des Gesamtsystems liefert.