Search result: Catalogue data in Autumn Semester 2018
Computational Science and Engineering TC Detailed information on the programme at: www.didaktischeausbildung.ethz.ch | ||||||
Educational Science General course offerings in the category Educational Science are listed under "Programme: Educational Science for Teaching Diploma and TC". | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
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851-0240-00L | Human Learning (EW1) This lecture is only apt for students who intend to enrol in the programs "Teaching Diploma" or "Teaching Certificate". It is about learning in childhood and adolescence. | O | 2 credits | 2G | E. Stern | |
Abstract | This course looks into scientific theories and also empirical studies on human learning and relates them to the school. | |||||
Learning objective | Anyone wishing to be a successful teacher must first of all understand the learning process. Against this background, theories and findings on the way humans process information and on human behaviour are prepared in such a manner that they can be used for planning and conducting lessons. Students additionally gain an understanding of what is going on in learning and behavioural research so that teachers are put in a position where they can further educate themselves in the field of research into teaching and learning. | |||||
Content | Thematische Schwerpunkte: Lernen als Verhaltensänderung und als Informationsverarbeitung; Das menschliche Gedächtnis unter besonderer Berücksichtigung der Verarbeitung symbolischer Information; Lernen als Wissenskonstruktion und Kompetenzerwerb unter besonderer Berücksichtigung des Wissenstransfers; Lernen durch Instruktion und Erklärungen; Die Rolle von Emotion und Motivation beim Lernen; Interindividuelle Unterschiede in der Lernfähigkeit und ihre Ursachen: Intelligenztheorien, Geschlechtsunterschiede beim Lernen Lernformen: Theorien und wissenschaftliche Konstrukte werden zusammen mit ausgewählten wissenschaftlichen Untersuchungen in Form einer Vorlesung präsentiert. Die Studierenden vertiefen nach jeder Stunde die Inhalte durch die Bearbeitung von Aufträgen in einem elektronischen Lerntagebuch. Über die Bedeutung des Gelernten für den Schulalltag soll reflektiert werden. Ausgewählte Tagebucheinträge werden zu Beginn jeder Vorlesung thematisiert. | |||||
Lecture notes | Folien werden zur Verfügung gestellt. | |||||
Literature | 1) Marcus Hasselhorn & Andreas Gold (2006). Pädagogische Psychologie: Erfolgreiches Lernen und Lehren. Stuttgart: Kohlhammer. 2) Jeanne Omrod (2006): Human Learning. Upper Saddle River: Pearson Prentice Hall. | |||||
Prerequisites / Notice | This lecture is only apt for students who intend to enrol in the programs "Lehrdiplom" or "Didaktisches Zertifikat". It is about learning in childhood and adolescence. | |||||
851-0240-03L | Introduction to Test Theory and Test Construction in Educational Contexts (University of Zürich) Enrolment only possible with Teaching Diploma or DC matriculation. No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH. UZH Module Code: 200c968 Mind the enrolment deadlines at UZH: https://www.uzh.ch/cmsssl/en/studies/application/mobilitaet.html | W | 4 credits | 2S | University lecturers | |
Abstract | The seminar will provide theoretical and applied knowledge in the construction of psychological questionnaires. The students will construct, translate, and adapt instruments from different areas. Afterwards, data on these instruments will be gathered in an online survey. The survey will be analyzed (under the guidance of the seminar leaders) and presented in a scientific report. | |||||
Learning objective | The learning outcomes are: - Acquiring theoretical and applied knowledge in the construction, translation, and adaption of psychological instruments - Conducting online surveys and statistical analyses - Becoming more familiar with relevant statistical procedures (e.g., factor analysis, reliability, correlation, and regression analyses) - Estimating and evaluating the psychometric properties of instruments - Scientific description and communication of the results (using APA-style) | |||||
Content | Die Lehrveranstaltung soll Studierenden theoretische und praktische Kenntnisse in der Konstruktion von Fragebogen vermitteln. Es werden Instrumente aus verschiedenen Bereichen durch die Studierenden konstruiert, übersetzt und adaptiert. Danach erfolgt eine Online-Erhebung dieser Instrumente, die anschliessend unter Anleitung ausgewertet und in einem wissenschaftlichen Bericht präsentiert wird. | |||||
Lecture notes | Alle Unterlagen werden im OLAT-Kurs zur Verfügung gestellt Voraussetzung für die Teilnahme ist ein eigener Laptop mit einem Statistikprogramm (z.B. SPSS) und einem Office-Paket. | |||||
Literature | Alle Unterlagen werden zur Verfügung gestellt. | |||||
Prerequisites / Notice | Der Leistungsnachweis besteht aus einem schriftlichen Leistungsnachweis, der benotet wird, ausserdem werden die unten genannten Aspekte von aktiver Teilnahme für das Bestehen des Moduls vorausgesetzt. Der schriftliche Leistungsnachweis besteht aus einem wissenschaftlichen Bericht zur psychometrischen Prüfung einer im Rahmen des Seminars selbst adaptierten, konstruierten oder übersetzten Skala. Die aktive Teilnahme besteht aus Vorbereitung auf die Sitzungen, Rekrutierung von Teilnehmenden für die gemeinsame Datenerhebung, zwei kurzen Präsentationen zur praktischen Aufgabe sowie aktiver Teilnahme am Seminar. Voraussetzung für die Teilnahme ist ein eigener Laptop mit einem Statistikprogramm (z.B. SPSS) und einem Office-Paket. | |||||
851-0240-16L | Colloquium on the Science of Learning and Instruction | W | 1 credit | 1K | E. Stern, P. Greutmann, further lecturers | |
Abstract | In the colloquium we discuss scientific projects concerning the teaching in mathematics, computer science, natural sciences and technology (STEM). The colloquium is conducted by the professorships participating in the Competence Center EducETH (ETH) and in the Institute for Educational Sciences (UZH). | |||||
Learning objective | Participants are exemplarily introduced to different research methods used in research on learning and instruction and learn to weigh advantages and disadvantages of these approaches. | |||||
851-0240-22L | Coping with Psychosocial Demands of Teaching (EW4 DZ) Number of participants limited to 20. The successful participation in EW1 ("Human Learning") and EW2 ("Designing Learning Environments for School") is recommended, but not a mandatory prerequisite. | W | 2 credits | 3S | A. Deiglmayr, P. Greutmann, U. Markwalder, S. Peteranderl | |
Abstract | In this class, students will learn concepts and skills for coping with psychosocial demands of teaching | |||||
Learning objective | Students possess theoretical knowledge and practical competences to be able to cope with the psychosocial demands of teaching. (1) They know the basic rules of negotiation and conflict management (e.g., mediation) and can apply them in the school context (e.g., in conversations with parents). (2) They can apply diverse techniques of classroom management (e.g., prevention of disciplinary problems in the classroom) and know relevant authorities for further information (e.g., legal conditions). | |||||
851-0242-06L | Cognitively Activating Instructions in MINT Subjects Enrolment only possible with matriculation in Teaching Diploma or Teaching Certificate (excluding Teaching Diploma Sport). This course unit can only be enrolled after successful participation in, or during enrollment in the course "Human Learning (EW 1)". | W | 2 credits | 2S | R. Schumacher | |
Abstract | This seminar focuses on teaching units in chemistry, physics and mathematics that have been developed at the MINT Learning Center of the ETH Zurich. In the first meeting, the mission of the MINT Learning Center will be communicated. Furthermore, in groups of two, the students will intensively work on, refine and optimize a teaching unit following a goal set in advance. | |||||
Learning objective | - Get to know cognitively activating instructions in MINT subjects - Get information about recent literature on learning and instruction | |||||
Prerequisites / Notice | Für eine reibungslose Semesterplanung wird um frühe Anmeldung und persönliches Erscheinen zum ersten Lehrveranstaltungstermin ersucht. | |||||
851-0242-08L | Research Methods in Educational Science Number of participants limited to 30 This course unit can only be enrolled after successful participation in, or during enrollment in the course "Human Learning (EW 1)". | W | 1 credit | 1S | P. Edelsbrunner, M. Berkowitz Biran, Z. Lue, C. M. Thurn | |
Abstract | Literature from the learning sciences is critically discussed with a focus on research methods. At the first meeting, working groups will be assembled and meetings with those will be set up. In the small groups students will write critical essays about the read literature. At the third meeting, we will discuss the essays and develop research questions in group work. | |||||
Learning objective | - Understand research methods used in the empirical educational sciences - Understand and critically examine information from scientific journals and media - Understand pedagogically relevant findings from the empirical educational sciences | |||||
851-0242-07L | Human Intelligence Enrolment only possible with matriculation in Teaching Diploma or Teaching Certificate (excluding Teaching Diploma Sport). Number of participants limited to 30. This course unit can only be enrolled after successful participation in, or during enrollment in the course "Human Learning (EW 1)". | W | 1 credit | 1S | E. Stern | |
Abstract | The focus will be on the book "Intelligenz: Grosse Unterschiede und ihre Folgen" by Stern and Neubauer. Participation at the first meeting is obligatory. It is required that all participants read the complete book. Furthermore, in two meetings of 90 minutes, concept papers developed in small groups (5 - 10 students) will be discussed. | |||||
Learning objective | - Understanding of research methods used in the empirical human sciences - Getting to know intelligence tests - Understanding findings relevant for education | |||||
Subject Didactics and Professional Training Important: You can only enrol in the courses of this category if you have not more than 12 CP left for possible additional requirements. | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
401-9908-00L | Teaching Internship Including Examination Lessons Computational Science and Engineering Teaching Internship Computational Science and Engineering for TC. The teaching internship can just be visited if all other courses of TC are completed. Repetition of the teaching internship is excluded even if the examination lessons are to be repeated. | O | 6 credits | 13P | J. Hromkovic, G. Serafini | |
Abstract | Students apply the insights, abilities and skills they have acquired within the context of an educational institution. They observe 10 lessons and teach 20 lessons independently. Two of them are as assessed as Examination Lessons. | |||||
Learning objective | - Students use their specialist-subject, educational-science and subject-didactics training to draw up concepts for teaching. - They are able to assess the significance of tuition topics for their subject from different angles (including interdisciplinary angles) and impart these to their pupils. - They learn the skills of the teaching trade. - They practise finding the balance between instruction and openness so that pupils can and, indeed, must make their own cognitive contribution. - They learn to assess pupils' work. - Together with the teacher in charge of their teacher training, the students constantly evaluate their own performance. | |||||
Content | Die Studierenden sammeln Erfahrungen in der Unterrichtsführung, der Auseinandersetzung mit Lernenden, der Klassenbetreuung und der Leistungsbeurteilung. Zu Beginn des Praktikums plant die Praktikumslehrperson gemeinsam mit dem/der Studierenden das Praktikum und die Arbeitsaufträge. Die schriftlich dokumentierten Ergebnisse der Arbeitsaufträge sind Bestandteil des Portfolios der Studierenden. Anlässlich der Hospitationen erläutert die Praktikumslehrperson ihre fachlichen, fachdidaktischen und pädagogischen Überlegungen, auf deren Basis sie den Unterricht geplant hat und tauscht sich mit dem/der Studierenden aus. Die von dem/der Studierenden gehaltenen Lektionen werden vor- und nachbesprochen. Die Themen für die beiden Prüfungslektionen am Schluss des Praktikums erfahren die Studierenden in der Regel eine Woche vor dem Prüfungstermin. Sie erstellen eine Vorbereitung gemäss Anleitung und reichen sie bis am Vortrag um 12 Uhr den beiden Prüfungsexperten (Fachdidaktiker/-in, Departementsvertreter/-in) ein. Die gehaltenen Lektionen werden kriteriumsbasiert beurteilt. Die Beurteilung umfasst auch die schriftliche Vorbereitung und eine mündliche Reflexion des Kandidaten/der Kandidatin über die gehaltenen Lektionen im Rahmen eines kurzen Kolloquiums. | |||||
Lecture notes | Dokument: schriftliche Vorbereitung für Prüfungslektionen. | |||||
Literature | Wird von der Praktikumslehrperson bestimmt. | |||||
272-0101-00L | Subject Didactics of Computer Science I Simultaneous enrolment in Introductory Practical in Computer Science - course 272-0201-00L - is compulsory. | O | 4 credits | 3G | G. Serafini, J. Hromkovic | |
Abstract | The unit "Subject Didactics of Computer Science I" addresses key contributions of computer science to general education. The course deals with the thoughtful choice of educational contents for computer science classes, which takes into account its comprehensibility for different age groups as well as didactic approaches suitable for a successful knowledge transfer. | |||||
Learning objective | The general objective of the course consists in highlighting the tight connection between the mathematical and algorithmic way of thinking and the approaches adopted by engineering disciplines, and in reflecting on teaching approaches for sustainable computer science teaching activities. The students understand the fundamental concepts of computer science in the context of a broad and deep knowledge. Through this understanding, they manage to prepare teaching materials for a successful knowledge transfer and to pass their passion for the subject on to their pupils. The students know various teaching methods as well as their advantages and disadvantages. They can handle inhomogeneous prior knowledge of the learners inside a class. Besides holding classes, the students do care about the individual pupil support. They encourage the autonomy of the learners, manage to work with diverse target groups and to establish a positive learning environment. The students are able to express themselves using a comprehensible and refined professional language, both in a spoken and a written way, and they master the basic terminology of computer science. Besides the English terms, they are familiar with the corresponding German expressions. The students are able to produce detailed, matured, linguistically correct and design-wise appealing teaching materials. | |||||
Content | The course "Subject Didactics of Computer Science I" addresses key contributions of computer science to general education. The chosen topics support the young learners in developing a unique and indispensable way of thinking, in enhancing their understanding of our world as well as in achieving university education entrance qualifications. The main topics of the course unit "Subject Didactics of Computer Science I" are the didactics of finite state automata, of formal languages and of the introduction to programming. The unit focuses on contents of computer science that contribute to general education. This involves the understanding of fundamental scientific concepts such as algorithm, complexity, determinism, computation, automata, verification, testing and programming language as well as the way to embed them into a scientifically sound and didactically sustainable computer science course. In a semester exercise, the students develop and document an adaptive teaching unit for computer science. They learn to employ the didactics methods and techniques that are introduced at the beginning of the semester. | |||||
Lecture notes | Unterlagen und Folien werden zur Verfügung gestellt. | |||||
Literature | J. Hromkovic: Sieben Wunder der Informatik: Eine Reise an die Grenze des Machbaren, mit Aufgaben und Lösungen. Vieweg+Teubner; Auflage: 2 (2008). K. Freiermuth, J. Hromkovic, L. Keller und B. Steffen: Einfuehrung in die Kryptologie: Lehrbuch für Unterricht und Selbststudium. Springer Vieweg; Auflage: 2 (2014). J. Hromkovic: Berechenbarkeit: Logik, Argumentation, Rechner und Assembler, Unendlichkeit, Grenzen der Automatisierbarkeit. Vieweg+Teubner; Auflage: 1 (2011). H.-J. Böckenhauer, J. Hromkovic: Formale Sprachen: Endliche Automaten, Grammatiken, lexikalische und syntaktische Analyse. Springer Vieweg; Auflage: 1 (Januar 2013). J. Hromkovic: Einführung in die Programmierung mit LOGO: Lehrbuch für Unterricht und Selbststudium. Springer Vieweg; Auflage: 3 (2014) | |||||
Prerequisites / Notice | Lehrdiplom-Studierende müssen diese Lerneinheit zusammen mit dem Einführungspraktikum Informatik - 272-0201-00L - belegen. | |||||
401-9901-00L | Mentored Work Subject Didactics Computational Science and Engineering | O | 2 credits | 4A | J. Hromkovic, G. Serafini | |
Abstract | In their mentored work on subject didactics, students put into practice the contents of the subject-didactics lectures and go into these in greater depth. Under supervision, they compile tuition materials that are conducive to learning and/or analyse and reflect on certain topics from a subject-based and pedagogical angle. | |||||
Learning objective | The objective is for the students: - to be able to familiarise themselves with a tuition topic by consulting different sources, acquiring materials and reflecting on the relevance of the topic and the access they have selected to this topic from a specialist, subject-didactics and pedagogical angle and potentially from a social angle too. - to show that they can independently compile a tuition sequence that is conducive to learning and develop this to the point where it is ready for use. | |||||
Content | Thematische Schwerpunkte Die Gegenstände der mentorierten Arbeit in Fachdidaktik stammen in der Regel aus dem gymnasialen Unterricht. Lernformen Alle Studierenden erhalten ein individuelles Thema und erstellen dazu eine eigenständige Arbeit. Sie werden dabei von ihrer Betreuungsperson begleitet. Gegebenenfalls stellen sie ihre Arbeit oder Aspekte daraus in einem Kurzvortrag vor. Die mentorierte Arbeit ist Teil des Portfolios der Studierenden. | |||||
Literature | Die Literatur ist themenspezifisch. Die Studierenden beschaffen sie sich in der Regel selber (siehe Lernziele). In besonderen Fällen wird sie vom Betreuer zur Verfügung gestellt. | |||||
Prerequisites / Notice | Die Arbeit sollte vor Beginn des Praktikums abgeschlossen werden. | |||||
Further Subject Didactics | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
263-2800-00L | Design of Parallel and High-Performance Computing | W | 7 credits | 3V + 2U + 1A | T. Hoefler, M. Püschel | |
Abstract | Advanced topics in parallel / concurrent programming. | |||||
Learning objective | Understand concurrency paradigms and models from a higher perspective and acquire skills for designing, structuring and developing possibly large concurrent software systems. Become able to distinguish parallelism in problem space and in machine space. Become familiar with important technical concepts and with concurrency folklore. | |||||
252-0535-00L | Advanced Machine Learning | W | 8 credits | 3V + 2U + 2A | J. M. Buhmann | |
Abstract | Machine learning algorithms provide analytical methods to search data sets for characteristic patterns. Typical tasks include the classification of data, function fitting and clustering, with applications in image and speech analysis, bioinformatics and exploratory data analysis. This course is accompanied by practical machine learning projects. | |||||
Learning objective | Students will be familiarized with advanced concepts and algorithms for supervised and unsupervised learning; reinforce the statistics knowledge which is indispensible to solve modeling problems under uncertainty. Key concepts are the generalization ability of algorithms and systematic approaches to modeling and regularization. Machine learning projects will provide an opportunity to test the machine learning algorithms on real world data. | |||||
Content | The theory of fundamental machine learning concepts is presented in the lecture, and illustrated with relevant applications. Students can deepen their understanding by solving both pen-and-paper and programming exercises, where they implement and apply famous algorithms to real-world data. Topics covered in the lecture include: Fundamentals: What is data? Bayesian Learning Computational learning theory Supervised learning: Ensembles: Bagging and Boosting Max Margin methods Neural networks Unsupservised learning: Dimensionality reduction techniques Clustering Mixture Models Non-parametric density estimation Learning Dynamical Systems | |||||
Lecture notes | No lecture notes, but slides will be made available on the course webpage. | |||||
Literature | C. Bishop. Pattern Recognition and Machine Learning. Springer 2007. R. Duda, P. Hart, and D. Stork. Pattern Classification. John Wiley & Sons, second edition, 2001. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, 2001. L. Wasserman. All of Statistics: A Concise Course in Statistical Inference. Springer, 2004. | |||||
Prerequisites / Notice | The course requires solid basic knowledge in analysis, statistics and numerical methods for CSE as well as practical programming experience for solving assignments. Students should have followed at least "Introduction to Machine Learning" or an equivalent course offered by another institution. | |||||
252-0417-00L | Randomized Algorithms and Probabilistic Methods | W | 8 credits | 3V + 2U + 2A | A. Steger | |
Abstract | Las Vegas & Monte Carlo algorithms; inequalities of Markov, Chebyshev, Chernoff; negative correlation; Markov chains: convergence, rapidly mixing; generating functions; Examples include: min cut, median, balls and bins, routing in hypercubes, 3SAT, card shuffling, random walks | |||||
Learning objective | After this course students will know fundamental techniques from probabilistic combinatorics for designing randomized algorithms and will be able to apply them to solve typical problems in these areas. | |||||
Content | Randomized Algorithms are algorithms that "flip coins" to take certain decisions. This concept extends the classical model of deterministic algorithms and has become very popular and useful within the last twenty years. In many cases, randomized algorithms are faster, simpler or just more elegant than deterministic ones. In the course, we will discuss basic principles and techniques and derive from them a number of randomized methods for problems in different areas. | |||||
Lecture notes | Yes. | |||||
Literature | - Randomized Algorithms, Rajeev Motwani and Prabhakar Raghavan, Cambridge University Press (1995) - Probability and Computing, Michael Mitzenmacher and Eli Upfal, Cambridge University Press (2005) |
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