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The prescriber’s guide to classic MAO inhibitors (phenelzine, tranylcypromine, isocarboxazid) for treatment-resistant depression
- Vincent Van den Eynde, Wegdan R. Abdelmoemin, Magid M. Abraham, Jay D. Amsterdam, Ian M. Anderson, Chittaranjan Andrade, Glen B. Baker, Aartjan T.F. Beekman, Michael Berk, Tom K. Birkenhäger, Barry B. Blackwell, Pierre Blier, Marc B.J. Blom, Alexander J. Bodkin, Carlo I. Cattaneo, Bezalel Dantz, Jonathan Davidson, Boadie W. Dunlop, Ryan F. Estévez, Shalom S. Feinberg, John P.M. Finberg, Laura J. Fochtmann, David Gotlib, Andrew Holt, Thomas R. Insel, Jens K. Larsen, Rajnish Mago, David B. Menkes, Jonathan M. Meyer, David J. Nutt, Gordon Parker, Mark D. Rego, Elliott Richelson, Henricus G. Ruhé, Jerónimo Sáiz-Ruiz, Stephen M. Stahl, Thomas Steele, Michael E. Thase, Sven Ulrich, Anton J.L.M. van Balkom, Eduard Vieta, Ian Whyte, Allan H. Young, Peter K. Gillman
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- Journal:
- CNS Spectrums / Volume 28 / Issue 4 / August 2023
- Published online by Cambridge University Press:
- 15 July 2022, pp. 427-440
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- Article
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This article is a clinical guide which discusses the “state-of-the-art” usage of the classic monoamine oxidase inhibitor (MAOI) antidepressants (phenelzine, tranylcypromine, and isocarboxazid) in modern psychiatric practice. The guide is for all clinicians, including those who may not be experienced MAOI prescribers. It discusses indications, drug-drug interactions, side-effect management, and the safety of various augmentation strategies. There is a clear and broad consensus (more than 70 international expert endorsers), based on 6 decades of experience, for the recommendations herein exposited. They are based on empirical evidence and expert opinion—this guide is presented as a new specialist-consensus standard. The guide provides practical clinical advice, and is the basis for the rational use of these drugs, particularly because it improves and updates knowledge, and corrects the various misconceptions that have hitherto been prominent in the literature, partly due to insufficient knowledge of pharmacology. The guide suggests that MAOIs should always be considered in cases of treatment-resistant depression (including those melancholic in nature), and prior to electroconvulsive therapy—while taking into account of patient preference. In selected cases, they may be considered earlier in the treatment algorithm than has previously been customary, and should not be regarded as drugs of last resort; they may prove decisively effective when many other treatments have failed. The guide clarifies key points on the concomitant use of incorrectly proscribed drugs such as methylphenidate and some tricyclic antidepressants. It also illustrates the straightforward “bridging” methods that may be used to transition simply and safely from other antidepressants to MAOIs.
13 - Learning Analytics and Educational Data Mining
- from Part II - Methodologies
- Edited by R. Keith Sawyer, University of North Carolina, Chapel Hill
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- Book:
- The Cambridge Handbook of the Learning Sciences
- Published online:
- 14 March 2022
- Print publication:
- 07 April 2022, pp 259-278
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Summary
In recent years, the use of analytics and data mining – methodologies that extract useful information from large datasets – has become commonplace in science and business. When these methods are used in education, they are referred to as learning analytics (LA) and educational data mining (EDM). For example, adaptive learning platforms – those that respond uniquely to each learner – require learning analytics to model the learner’s current state of knowledge. The researcher can conduct second-by-second analyses of phenomena that occur over long periods of time or in an individual learning session. Large datasets are required for these analyses. In most cases, the data are gathered automatically – such as keystrokes, eye movement, or assessments – and are analyzed using algorithms based in learning sciences research. This chapter reviews prediction methods, structure discovery, relationship mining, and discovery with models.
Chapter 14 - Adaptive Learning
- Edited by Michael McCarthy
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- Book:
- The Cambridge Guide to Blended Learning for Language Teaching
- Published online:
- 22 September 2021
- Print publication:
- 18 February 2015, pp 234-247
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Summary
INTRODUCTION
As discussed throughout this book, blended learning (BL) has become an increasingly important part of modern education, from schoolchildren to higher education and adult learners. In a BL environment, students learn in part at their own time and pace through instruction and content delivered online, as well as learning in part through supervised face-to-face instruction in a school or higher education context (Garrison and Vaughan, 2008 ; Graham, 2006). This blend can vary in the degrees of face-to-face and online instructional content depending on the subject matter, students and instructor.
One of the key opportunities of BL is to support better personalisation for the range of individual differences which students bring to learning situations. For example, students may vary in terms of their aptitudes, interests and motivations (Cordova and Lepper, 1996 ; Grant and Basye, 2014). To help students achieve their optimal learning potential, educators must be able to offer personalised learning experiences that customise the instruction given to students to meet their individual needs and goals for learning. Such differentiation of instruction must then address each student's ability, interest and motivation.
Personalised learning in instruction development does not necessarily require technology (e.g., Clarke and Miles, 2003), but the arrival of modern computerised learning environments increases the potential for individualisation, both through providing better data to teachers and instructional designers, and by making it economically feasible to design student models and interventions that may apply to a relatively small proportion of students (Ben-Naim et al., 2007 ; Graesser et al., 2007 ; Koedinger and Corbett, 2006 ; Mayer et al., 2004 ; VanLehn, 1996). In integrating technology within a traditional classroom instruction model, BL takes advantage of these opportunities for personalised learning, while maintaining the affordances of classroom activities. Within well-implemented BL, students are provided with technology that enables them to learn the material at their own pace, lets them individually experience success and failure with the material and informs them about their learning progress. The affordances of such technology include capturing student learning experiences in real time and reporting on students’ performance to the teacher, giving the teacher opportunities to provide more immediate feedback relevant to the students’ learning state.
Contributors
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- By Dor Abrahamson, Jerry Andriessen, Roger Azevedo, Michael Baker, Ryan Baker, Sasha Barab, Carl Bereiter, Susan Bridges, Mario Carretero, Carol K. K. Chan, Clark A. Chinn, Paul Cobb, Allan Collins, Kevin Crowley, Elizabeth A. Davis, Chris Dede, Sharon J. Derry, Andrea A. diSessa, Michael Eisenberg, Yrjö Engeström, Noel Enyedy, Barry J. Fishman, Ricki Goldman, James G. Greeno, Erica Rosenfeld Halverson, Cindy E. Hmelo-Silver, Michael J. Jacobson, Sanna Järvelä, Yasmin B. Kafai, Yael Kali, Manu Kapur, Paul A. Kirschner, Karen Knutson, Timothy Koschmann, Joseph S. Krajcik, Carol D. Lee, Peter Lee, Robb Lindgren, Jingyan Lu, Richard E. Mayer, Naomi Miyake, Na’ilah Suad Nasir, Mitchell J. Nathan, Narcis Pares, Roy Pea, James W. Pellegrino, William R. Penuel, Palmyre Pierroux, Brian J. Reiser, K. Ann Renninger, Ann S. Rosebery, R. Keith Sawyer, Marlene Scardamalia, Anna Sfard, Mike Sharples, Kimberly M. Sheridan, Bruce L. Sherin, Namsoo Shin, George Siemens, Peter Smagorinsky, Nancy Butler Songer, James P. Spillane, Kurt Squire, Gerry Stahl, Constance Steinkuehler, Reed Stevens, Daniel Suthers, Iris Tabak, Beth Warren, Uri Wilensky, Philip H. Winne, Carmen Zahn
- Edited by R. Keith Sawyer, University of North Carolina, Chapel Hill
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- Book:
- The Cambridge Handbook of the Learning Sciences
- Published online:
- 05 November 2014
- Print publication:
- 17 November 2014, pp xv-xviii
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