Microsoft Word - WPM$0CF8.doc

....................5 2.2.1.1 Respondente Konditionierung - Lernen durch Erfahrungen ............5 2.2.1.2 Kontingente Konditionierung - Lernen durch Verknüpfungen ........6 2.2.1.3 Operante Konditionierung [...] ...6 2.2.2 Paradigma und Grenzen des Behaviorismus................................................7 2.2.3 Einsatz in computergestützten Lernumgebungen ........................................8 2.3 Kog [...] ............................................3 2.2 Behaviorismus - eine empirische Lerntheorie .......................................................4 2.2.1 Behavioristische Konditionierungen ........ …

Wirksamkeit problembasierten Lernens als hochschuldidaktische Methode

(N=21) (N=141) 2,12 1,228 0 4 (N=114) 2,62 0,733 1 4 Mittelwerte 2,21 2,00 - - 2,24 (N=110) 2,23 1,170 0 5 (N=98) 2,50 0,922 1 5 Mittelwerte - - 2,06 2,16 - (N=251) 2,17 1,202 0 5 (N=212) 2,57 0,826 1 5 [...] KS-Test=1,268; p=0,080; ˔ 2 =12,990, df=2; p=0,002 ˔ 2 =1,450, df=1; p=0,228 ˔ 2 =1,900, df=1; p=0,168 trifft nicht zu ˔ 2 =11,227, df=1; p=0,001 Kruskal-Wallis- ˔ 2 =4,102, df=2; p=0,129; Rho=0,044, p=0 [...] 001 Lesehäufigkeit Zeitungen 269 2,44 1,113 3,519 p<0,001 Item 5.1 252 3,41 0,881 4,014 p<0,001 Item5.2 266 2,80 1,099 2,930 p<0,001 Item 5.3 266 2,59 1,130 2,882 p<0,001 Item 5.4 270 3,91 0,887 4,490 …

Proseminar „Ergonomie und Computerzugänglichkeit” (PS2) — Lehrstuhl 11 Algorithm Engineering

beim Mensch (1) 09.12.2021: [Phase2] Kognitive Mechanismen beim Mensch (2) 16.12.2021: [Phase2] Kognitive Mechanismen beim Mensch (3) 23.12.2021: [Phase2] Kognitive Mechanismen beim Mensch (4) 13.01.2022: [...] von HID und deren Wandel (2) 18.11.2021: [Phase1] Technologie von HID und deren Wandel (3) 25.11.2021: [Phase1] Technologie von HID und deren Wandel (4) 02.12.2021: [Phase2] Kognitive Mechanismen beim [...] Proseminar „Ergonomie und Computerzugänglichkeit” (PS2) — Lehrstuhl 11 Algorithm Engineering English Deutsch Search You are here:: TU Dortmund > Department of Computer Science > Chair 11: ALGORITHM ENGINEERING …

<https://eldorado.tu-dortmund.de/bitstreams/23ead2f2-a157-4815-a90a-a909721c1c37/download> ; rel="item" ; type="application/pdf" ; anchor="https://eldorado.tu-dortmund.de/items/1807572f-da31-4531-bcb1 [...] rel="linkset" ; type="application/linkset" ; anchor="https://eldorado.tu-dortmund.de/bitstreams/23ead2f2-a157-4815-a90a-a909721c1c37/download" , <https://eldorado.tu-dortmund.de/signposting/linksets/1807 [...] rel="linkset" ; type="application/linkset+json" ; anchor="https://eldorado.tu-dortmund.de/bitstreams/23ead2f2-a157-4815-a90a-a909721c1c37/download" , <https://eldorado.tu-dortmund.de/items/1807572f-da31-4531- …

which the following graph is a t-spanner? A: √ 2 B: 2 C: √ 2 + 1 Quiz What is the smallest t for which the following graph is a t-spanner? A: √ 2 B: 2 C: √ 2 + 1 Quiz What is the smallest t for which the [...] the following graph is a t-spanner? A: √ 2 B: 2 C: √ 2 + 1 How can we compute a t-spanner? Spanner construction paradigms greedy • sort point pairs by distance, start with no edges • if for the next point [...] WSPD of size O(n2) is trivial. What is the ‘size’? Can we get size O(n)? Quiz What size does a 2-WSPD on the following point set have at least? A: 3 B: 4 C: 5 D: 6 Quiz What size does a 2-WSPD on the following …

satisfaction proba- bility, and so on. Example 4. In Examples 1 and 2 we have F1 ↑ = F2 ↑ = (1, 2 3 , 2 3 , 2 3 ) ≻ D ↑ = ( 23 , 2 3 , 2 3 , 2 3 ). As D ↑ is not lexicographically maximal, it cannot be maxmin- [...] the fact that F ↑ [D1 ∪ D2] ↑ implies F2 ↑ D2 ↑, and the maxmin-fairness of D2 allows us to deduce that F2 ↑= D2 ↑. Hence, by Lemma 1, for all v /∈ B1 we have F [v] = F2[v] = D2[v]. Similarly, we have the [...] probabilities of a0, a1, a2 and a3 are then 1, 2 3 , 2 3 , 2 3 . Any attempt to match, say, a1 with probability > 2 3 will necessarily result in satisfaction probabil- ity < 2 3 for a2 or a3. Another maxmin-fair …

Distance Functions – Lecture Notes

Distance Functions /2 Many more variants: Squared Euclidean distance ( sum of squares ): \[\begin{align*} d_{\operatorname {Euclidean}}^2(x,y) =& \kern 5pt \sum \nolimits _d (x_d - y_d)^2 \quad = \left\lVert [...] y\right\rVert } = \tfrac { \sum _i x_i y_i }{ \sqrt{\sum _i x_i^2} \cdot \sqrt{\sum _i y_i^2} }\] only (!) on data normalized to \(L_2\) length \(\left\lVert x\right\rVert =\left\lVert y\right\rVert =1\) [...] where \(o_1\) has 1 and \(o_2\) has 0 \(f_{00}\) = the number of attributes where \(o_1\) has 0 and \(o_2\) has 0 \(f_{11}\) = the number of attributes where \(o_1\) has 1 and \(o_2\) has 1 Simple matching …

Distance Functions – Lecture Notes

Distance Functions /2 Many more variants: Squared Euclidean distance ( sum of squares ): \[\begin{align*} d_{\operatorname {Euclidean}}^2(x,y) =& \kern 5pt \sum \nolimits _d (x_d - y_d)^2 \quad = \left\lVert [...] y\right\rVert } = \tfrac { \sum _i x_i y_i }{ \sqrt{\sum _i x_i^2} \cdot \sqrt{\sum _i y_i^2} }\] only (!) on data normalized to \(L_2\) length \(\left\lVert x\right\rVert =\left\lVert y\right\rVert =1\) [...] where \(o_1\) has 1 and \(o_2\) has 0 \(f_{00}\) = the number of attributes where \(o_1\) has 0 and \(o_2\) has 0 \(f_{11}\) = the number of attributes where \(o_1\) has 1 and \(o_2\) has 1 Simple matching …