Optimizing Knowledge Component Learning Using a Dynamic Structural Model of Practice
|
|
- Morris Cameron
- 6 years ago
- Views:
Transcription
1 I Proceedigs of ICCM Eighth Iteratioal Coferece o Cogitive Modelig Oxford, UK: Taylor & Fracis/Psychology Press. Optimizig Kowledge Compoet Learig Usig a Dyamic Structural Model of Practice Philip I Pavlik Jr. (ppavlik@cs.cmu.edu), Huma Computer Iteractio Istitute Nora Presso (presso@cmu.edu), Psychology Departmet Keeth Koediger (koediger@cmu.edu), Huma Computer Iteractio Istitute Pittsburgh Sciece of Learig Ceter Caregie Mello Uiversity, 5000 Forbes Ave., Pittsburgh, PA 523 USA Abstract This paper presets a geeralized scheme for modelig learig i simple ad more complex tasks, ad shows how such a model ca be applied to optimizig coditios of practice to imize some desired performace. To eable this optimal allocatio of lesso time, this paper describes how to quatify the prefereces of studets usig utility fuctios that ca be imized. This covetioal game theoretic approach is eabled by specifyig a mathematical model that allows us to compute expected utility of various studet choices to choose the choice with imal expected utility. This method is applied to several educatioal decisios that ca beefit from optimizatio. Keywords: Memory; Ecoomics; Practice; Computer-Aided Istructio. Itroductio This paper describes a method for applyig ecoomic priciples i order to allocate the scarce resource of learig time toward satisfyig the ulimited eed for educatio. To do this, we describe a model that decomposes learig ito idividual kowledge compoets (KCs) that possess some degree of idepedece from other skills (a kowledge compoet is ay proficiecy that ca be leared). By assumig this idepedece, the model accouts for the uique effects of practice o specific KCs, with the goal of optimizig the beefit of practice. We do ot argue that the model is a precise represetatio of all the processes ivolved i learig, but rather that it provides a heuristic tool to track observed stregths of KCs as a geeral fuctio of practice, so that improvemet over time ad across KCs ca be optimized. The model we will preset, like similar models, is effective i capturig practice effects (Ce, Koediger, & Juker, 2006). Further, it is iterestig to ote that the dyamic practice model preseted here (based o the ACT-R computatioal model of declarative memory, Aderso & Lebiere, 998) might be substituted with aother model of cogitio with oly miimal modificatio to the approach. Although the model is a simplificatio of learig processes i most cases, this simplicity provides a importat advatage i applicatio. It allows closed form predictios of which learig evets (LEs) might be assiged at what times to imize learig (a learig evet is ay discrete iterval over which a leared proficiecy icreases). Ultimately, it is explaiig this collectio of closed form predictios ad recommedatios that is the goal of this paper. To explai these cocepts this paper has three parts. The first sectio o the dyamic practice model is largely a review of the ACT-R model of declarative memory. This sectio serves to oriet the reader o the output fuctios (probability ad latecy of recall) that will be used later. The secod sectio o structural models details how compoud evets ca be modeled usig the dyamic practice model. Compoud evets are importat to cosider whe resposes are ot idepedet ad are especially relevat for certai kids of optimizatio situatios (i.e. part-task to whole-task trasfer of performace). The fial sectio shows several ways the model built i the first half of the paper ca be applied to optimizig kowledge compoet learig. Dyamic Practice Model To uderstad the quatitative model that will be used to predict ad optimize learig, we will begi with the equatios that predict probability of correct performace ad latecy of correct performace as a fuctio of the activatio stregth of a KC. Probability Correct. The first depedet measure of KC performace is probability of correct respose. Equatio shows the stadard Boltzma equatio (similar to the Rasch model used i item respose theory), a logistic fuctio that characterizes the threshold of correct performace (the level of activatio at which performace is correct greater tha 50% of the time) ad distributioal oise as τ ad s respectively. Equatio describes a model of the probability of givig a correct respose (p) for a give KC activatio stregth value (m) ad the parameters described above. p m = τ + e m s Equatio Latecy. A secod depedet measure used to track KC performace is latecy (labeled q i our model). Various sources suggest modelig latecy with a Weibull distributio (Aderso & Lebiere, 998; Loga, 995). Such a Weibull distributio ca be produced by usig Equatio 2 to represet latecy as a fuctio of F (which scales latecy magitude), m (memory stregth) ad a fixed cost (which is determied from data ad captures the
2 miimum time ecessary for perceptual ad motor costs of respodig). Logistic oise o m determies the shape of the aggregate Weibull fuctio for a populatio of respose latecies. q m = Fe m + fixedtimecost Equatio 2 Kowledge Compoet Stregth Fuctio Give these two output fuctios, which correspod to two importat ways of measurig KC performace, we ca ow elaborate how curret m is computed as a fuctio of the history of a studet s practice of a KC practice item across prior LEs. Equatio 3 shows this KC stregth fuctio. The history term, the fial portio of Equatio 3, is essetially described by three values, t, d ad b, for each LE. The values for t represet the times sice each past LE (the ages of each LE effect). The d values are the power law decay values for each LE. The b values scales the effect of each LE depedig o the amout of learig for the LE (i.e. loger duratio LEs ad successful test LEs result i higher bs). To model history, the bt -d quatity is summed for each of the learig evets (LEs). The logarithm serves to scale the quatity from to. This power law decay formulatio was first explored by Aderso ad Schooler (99), who showed that it results i patters of forgettig that match the relative eed for performace i the eviromet. The β parameters, the first portio of Equatio 3, capture aturally occurrig error whe the model is fit to data from multiple studets or multiple KCs. β s is the parameter that captures cosistet error across KCs as a fuctio of studet. β i captures cosistet error across studets as a fuctio of KC (i stads for item). Fially β si captures the residual error for a specific KC ad a specific studet over multiple LEs. d m = β s + β i + β si + l bk t k k Equatio 3 k= b study =g( e v studyduratio ) Equatio 4 Equatio 4 shows how b ca be computed as a fuctio of the duratio of a study LE (where v ad g represet a growth costat ad the imum possible ecodig respectively). This captures the otio that cotiuous time spet o a sigle KC has a dimiishig effect o learig (Metcalfe & Korell, 2003). Recet work by Pavlik (i press) has show how this b scalar ca be used to capture the learig differece betwee active correct respodig ad passive study. I such work, b successfulretrieval is typically set at a costat, whereas b study varies as described i equatio 4. This supposes two caoical forms of the LE: the study LE, which comes from uassessed study over some fixed period of time of a stimulus represetig a KC, ad the test LE, which comes from a variable-duratio assessmet of learig (test LEs are ofte followed by study opportuity ad the are called drill LEs ). Test LEs are iterestig ot oly because they ted to lead to more learig tha passive study (for correct resposes), but also because they provide iformatio about the curret state of learig that ca be used to implemet kowledge tracig. Such kowledge tracig algorithms have chaged form over differet applicatios of the model. I the origial versio (Pavlik Jr., 2005), the distributio of residual β si variace is used as the iitial Bayesia prior for item stregth ad umerical itegratio is used to adjust this value after each practice by itegratig the logistic distributio for correctess give the respose of the studet. I the more recet versio, we have foud that a more computatioally iexpesive model that allows the simpler b successfulretrieval parameter to capture the β si variace works well i practice (Pavlik Jr. et al., 2007). Further, the latest versio also uses a b latecy parameter multiplied by each b successfulretrieval parameter for each successful test. This b latecy parameter is a atural log trasform (with a scalar parameter) of the differece betwee q m (the predicted latecy) ad the latecy data from the studet. This creates a kowledge tracig model that assumes that faster respodig meas more learig has occurred. Equatio 5 shows a more recet modificatio of the ACT- R equatios to capture the spacig effect, the spacig-bypractice iteractio, ad the spacig-by-retetio iterval iteractio (Pavlik Jr. & Aderso, 2005). This chage says that the forgettig rate from ay LE depeds o the level of activatio at the time of the LE. As modeled i Equatio 5, whe spacig betwee trials gets wider, activatio decreases betwee presetatios; decay is therefore less for each ew presetatio, ad log-term probability of correct performace does ot decrease as much. I Equatio 5, the decay rate d k is calculated for the kth presetatio of a KC item as a fuctio of the activatio m k- at the time the presetatio occurred (e.g., the decay rate for the 7th LE (t 7 ) depeds o the activatio at the time of the 7th LE, which is a fuctio of the time from last exposure of the prior 6 LEs ad their decay rates. It is importat to ote that sice t k s are ages (or differeces betwee the curret time ad the time of the past trial), activatio ad decay deped o the curret time as well as the umber of LEs). d m k = ce m k + a Equatio 5 Aderso, Ficham, ad Douglass (997) foud that Equatio 3 could accout for practice ad forgettig durig a experimet, but it could ot fit retetio data over log itervals. Because of this, they cocluded that betwee sessios, the presece of iterveig evets erodes KCs more slowly tha durig a experimetal sessio. This slower forgettig was modeled by scalig time as if it were slower outside the experimet. Forgettig is therefore depedet o the psychological time betwee presetatios, rather tha the true itersessio iterval. This factor is implemeted by multiplyig the portio of time that occurs betwee sessios by h (a scalar parameter for time) whe calculatig recall. This is doe by subtractig h*total itersessio time from each age (t k ) i Equatio (Pavlik Jr., 2005; Pavlik Jr. & Aderso, 2005). Because of this mechaism, time i the model is essetially a measure of destructive iterferig evets. The decay rate, therefore, is a measure of fragility of memories to the corrosive effect of these other evets. 2
3 This model has the flexibility to capture may varieties of learig ad practice effects. To further uderstad this flexibility, cosider the issue of more implicit productio rule (procedural) learig i cotrast to explicit factual (declarative) learig. This distictio is supported by research from widely distict theoretical perspectives such as ACT-R ad coectioism ad is supported by dissociable eural mechaisms (McClellad, McNaughto, & O'Reilly, 995). We might woder whether the equatios just preseted are adequate to capture both kowledge (ad KC) types. Specifically, that work implies that declarative learig is both faster (reflected by a larger b parameter) ad more easily forgotte (reflected by a larger d parameter) tha procedural learig, ad our model ca clearly characterize these differeces. Structural Model The structural level model assumes that few domais are made up of etirely idepedet KCs, as seems to be implied i the model we just preseted. The word structural refers to the fact that, because of this lack of idepedece, the modeler must be cocered with the structure that liks the multiple KCs ad their associatio. I may domais, predictios of the probability of correct respose ad latecy are derived from the stregth of more tha oe uderlyig KC. For example, i studies of Chiese vocabulary learig, stimuli ca be preseted i oe of four modes (Hazi character, piyi text, soud file, ad Eglish text). This results i 6 possible test LE types, two of which are Eglish piyi ad Hazi piyi drill LEs [(stimulus) (respose)]. I both of these cases, drill success depeds ot oly o the stregth of the lik betwee the stimulus ad respose, but also o the ability to recall ad produce the piyi respose. Because of this, performace for these pairs caot be idepedet. Similarly, i work with a Frech geder idetificatio task, words fall ito geder categories based o spellig ad sematic cues. For istace, words that ed i age are most ofte masculie i Frech, as i le fromage. Although each of these words might yield a correct respose idepedet of the geeral rule (through recall), it is also obvious that all rule exemplars share a KC that ca be used to respod to ay items i a cue category (ad i fact, it is this geeralized respodig, rather tha exemplar-based recall, that we wat to optimize). To deal with the fact that multiple KCs are required for these sigle skills, we will propose two basic structural models that accout for this, each of which fits some possible learig tasks: the cojuctive structure ad the disjuctive structure. Cojuctive Model I a cojuctive model, all compoet KCs must be active to produce a correct respose. For istace, i the Chiese vocabulary work, probability of correct performace for each trial is captured by the probability of correct recall for both the respose ad the lik betwee the stimulus ad the respose. Give this model, probability correct depeds o both the stregth of the lik ad the stregth of the respose i a cojuctive fuctio: p(lik) * p(respose), such that both elemets are ecessary for a correct respose. The more geeral form for the cojuctio of 2 KCs is show i Equatio 6. Latecy, o the other had, is hadled as the sum of the perceptual motor costs, the cost for recall of the lik KC, ad the cost for recall of the respose KC. Not oly does this structural model hadle the piyi respose example above, but it also captures data showig that respodig with a word i the ative laguage should be easier tha the recetly leared foreig equivalet (e.g. Scheider, Healy, & Boure, 2002). p( KC adkc 2 ) =p( KC ) p( KC 2 ) Equatio 6 Disjuctive Model The disjuctive model, i cotrast, assumes that a trial ca yield a correct respose due to performace of ay oe of the two or more idepedet KCs. Ofte disjuctive models apply i a geeralizatio situatio where the domai cotais specific KCs that apply for idividual stimuli ad geeral KCs that each apply to a group of stimuli, as i the Frech geder case. I this example, we ca imagie that geeral group KCs cotrol performace for clusters, the members of which ca also be leared by rote. Give the example of a geeral (rule-based) ad specific (rote) compoet cotrollig each performace, probability of correct skill performace depeds o the stregth of both geeral ad specific compoets i a disjuctive fuctio, p(geeral) + p(specific) * (-p(geeral)), such that (for example) a studet could classify a ovel word o the sole basis of the geeral KC. The geeral form of this model is show i Equatio 7. p( KC orkc 2 ) =p( KC ) + p( KC 2 )( p( KC ) Equatio 7 Optimizig Learig The followig procedures describe how oe ca use the model to compute optimal practice schedules. Usually, we assume that what is beig optimized is gai i some logterm measure of learig for a KC or multiple KCs. Although usig log-term probability correct as a depedet measure works whe we focus o optimizig some global aggregate task (like the optimal total umber of practices for a item), we eed a differet utility fuctio for more dyamic local schedulig (such as pickig a item to practice ext), i order to formalize prefereces for the learig gais from differet LE schedules. Utility Optimizatios We propose to use Equatio 8 as the utility fuctio for a LE (where b cotrols the weight of the LE, t is the desired retetio iterval of the LE, ad decay (d) is a fuctio of the activatio (m) at the time of practice). Most importatly, Equatio 8 does ot have the all-or-oe property of probability correct (because probability correct is a sigmoid 3
4 fuctio, it usually approaches 0 or ). If we tried to use log-term probability correct as our measure of local utility, it would value practice most heavily whe it comes ear the trasitio from mostly icorrect performace to mostly correct performace across a sequece of test LEs (those LEs that fall o the itermediate part of the curve). This bias distorts the fact that we are ultimately more cocered with the miimum umber of practice trials required to reach a certai log-term retetio, ot schedulig each practice trial so that it icreases percet correct imally. These goals are actually quite differet sice log-term percet correct gai from the ext practice depeds o earess to log-term floor or ceilig performace, while utility gai is ot affected by these bouds. Thus, our utility fuctio imizes the overall goal by valuig LEs idepedetly of the order they occurred, cosiderig oly their uique cotributios (a fuctio of stregth of ecodig, rececy, ad the decay rate) to the log-term KC stregth. u = bt d m Equatio 8 We will use Equatio 8 as a cardial utility fuctio: e.g., a.2 icrease i stregth is half as good as a.4 icrease i stregth. Oe reaso why this assumptio is reasoable is because LEs cotribute to KC stregth i small icremets ad these icremets are iterchageable, as illustrated i Equatio 3. Usig a cardial utility fuctio allows us to directly compare differet possible spacigs ad KC presetatio orders, to determie whe learig is imal, give learig history. Further, we assume that this utility equatio satisfies the vo Neuma ad Morgester game theoretic axioms of completeess, trasitivity, cotiuity ad idepedece required for comparig expected utility lotteries (Vo Neuma & Morgester, 944). Practice Spacig Optimizatio (PSO). For each KC ad each studet, it is useful to decide whe it would optimal to repeat a drill LE of that KC. Therefore, we are tryig to schedule the LEs uder coditios of allocative efficiecy. I ecoomics, allocative efficiecy is a coditio where costs (time spet learig) are allocated i a way that imizes gais (icreases i utility). Takig this parallel to learig theory, we search for the retetio iterval (for each KC) at which the expected rate of learig utility gai is imal give a ew LE. This is expressed i Equatio 9, which calculates the imum utility gai for a KC as a fuctio of m (activatio of that KC) ad t (the target retetio iterval eeded to compute g i Equatio 8). All the other values are fixed parameters (b s = success LE weight from Equatio 8 if the test LE is successful, b f = failure LE weight from Equatio 8 for the study LE give as review, -d computed from the curret m (eeded with t, b f ad b s to compute u values), p m ad q m estimated for the test LE from Equatios ad 2, ad failure costs estimated from prior data). Because t ad m are the oly values that vary i fidig the optimum spacig, we ca solve for the optimal level of the oe give the other. For example, if we kow the desired retetio iterval, we ca solve for the of Equatio 9 to solve for the optimal level of activatio at d mk = ce m k + a m = β s u = bt d m + β i + β si + l q m = Fe m + (, ) PSO Task PSO Task2 bk t k d k k= fixedtimecost p m u b s + ( p m ) u b f p m q m + ( p m ) failurecost p m = p m b study =g( e v studyduratio ) g( e vstudyduratio ) studyduratio + fixedcost τ + e m s p m expectedfrequecy ( T w PSO w [ p p p w, q p + q w ]) Figure. Orgaizig diagram of the mathematical relatioships i this paper. 4
5 which practice should occur. I practice, Equatio 9 teds to suggest (for a drill procedure) that whe failure costs for errors ad error feedback are high, or success gais from correct respodig are much greater tha failure gais from feedback study, log-term gais i utility per secod of practice will be highest whe repetitios are scheduled so that test LE performace is maitaied at a high probability. However, because the decay parameter ca be large for a LE after a short spacig, some spacig is always preferred. p m u b s + ( p m ) u bf p m q m + ( p m ) failurecost Equatio 9 Learig Evet Type Optimizatio. The above discussio assumes a sigle task (drill) which ca be selected for each item. However, we ca also propose other types of LEs ad the compare them with the drill trial. For example, we could decide whether it was better to give a study LE aloe or to give a drill LE( a test LE followed by a study LE whe the test fails). To do this, Equatio 0 shows how we ca compare the learig rates for each trial type to determie the optimal ext trial type for the studet. This priciple ca be exteded to compare ay two tasks (e.g., tutored problem solvig vs. ututored problem solvig). This is typically used i combiatio with dyamic PSO calculatios (whe the PSOs i Equatio 0 are computed as a fuctio of the curret time) to pick the optimal time for the optimal task. (, ) Equatio 0 PSO Task PSO Task2 Part- to Whole-Task Trasfer Optimizatio. For this optimizatio, the questio is whether to practice oly sigle KC compoets of a whole skill (a cojuctive skill cotaiig at least 2 KCs), oly the whole skill or some mixture of the two types of practice. Imagie, for example, practicig simple algebra, ad cosider that a compoet of the whole task may be kowig the times tables (the low level compoet). I this case, the questio is how much practice should be allocated to times tables practice before doig algebra practice (the high level compoet). We might expect that either spedig o time o times tables or o time o algebra would likely result i poorer algebra performace tha some mixture of these extremes, ad that a optimal mixture would allow for the best possible algebra performace. Part to whole trasfer optimizatio allows us to determie this optimal mixture. To compute this optimal mixture, we model the effect of the low level compoet LEs o the high level compoet learig rate. To do this, we must create a equatio expressig whole task learig as a fuctio of part task learig. Equatio (where subscripts w ad p refer to whole ad part task respectively) captures the otio that we are lookig to imize whole task time (T w ) * learig rate from a optimally spaced LE, which equals the total learig (this method assumes that all practice occurs at the PSO optimal poit). Here we specify that PSO for a cojuctive task is a fuctio of the stregth of the whole (depedet) KC ad the probability ad latecy estimates for the part task. By doig this, we have created a ew versio of the PSO, PSO w, that depeds o the stregth of both the part ad whole task KCs. At the same time, we are oly cocered with the learig of the whole task, so i practice, the t (retetio iterval) ad g (utility gai) terms are ot chaged from the origial PSO. This provides a mechaism whereby the higher probability ad lower latecy for a practiced part task icreases the expected stregth of the PSO w. (T w PSO w [ p p p w, q p + q w ]) Equatio Havig this mechaism, we ca compute the time eeded to trai the part task to imize its effects o whole task learig. I this case, it ca be oted that totaltime-t w is spet o the part task, with a learig rate of PSO p ; these values, therefore, cotrol p p (probability correct) ad q p (latecy). This allows us to costruct Equatio, which represets total learig as a fuctio of time spet o the whole task, multiplied by the learig rate for the whole task (which, because of the cojuctive respose fuctios i the PSO w, is itself a fuctio of time spet o the part task multiplied by the part task learig rate). Equatio ca the be solved for T w where T w 0 ad T w totaltime. Practice Legth Optimizatio Practice legth optimizatio determies the optimal duratio of a give LE. PLO relies o the fact that KC study for each LE has dimiishig margial returs as a fuctio of time as show i various studies (Metcalfe & Korell, 2003; Pavlik Jr., i press). Equatio 2 shows how this optimal study duratio is foud whe the total LE weight score (from Equatio 4) divided by the time spet studyig is imized. (Equatio 2 assumes some miimum study duratio greater tha 0 to accout for fixed costs.) g( e v studyduratio ) Equatio 2 studyduratio + fixedcost Practice Quatity Optimizatio Practice quatity optimizatio uses probability correct for log-term practice as a utility measure, the determies how may optimally spaced repetitios it takes to reach the poit where probability gai per LE is imal (the practice quatity optimizatio poit is the p m value whe Equatio 3 is imized) for each item beig leared of a set of items. p m Equatio 3 Figure 2 graphs Equati o 3 for the parameter set i Pavlik Jr. (2005, Experimet 4) where it was foud that practices would have bee optimal for each KC, as the imum value of the probability correct/practices curve occurs at repetitios. It is useful to ote that the utility fuctio should reflect the ature of our prefereces for target kowledge. For example, if the eed for oe KC is higher tha others, the gettig it correct has a higher utility. 5
6 Probability Correct Practices Probability Correct Probability Correct/ Practices Figure 2. Practice quatity optimizatio. To imp lemet this i the model, for istace, we ca weight the utility fuctio by the expected frequecy of the item we are iterested i. This captures the otio that it is twice as importat to kow a word whe that word is used twice as frequetly. Havig weighted the utility fuctios, we could the determie a cutoff word frequecy below which we will ot be cocered with learig the word (this fixes the total amout of time we will eed to sped learig the corpus i questio). p m expectedfrequecy Equatio 4 Because the weights represet our prefereces, other ways of weightig the relative values of differet distributios of practice amogst items might further improve the usefuless of such procedures i implemetatio. For example, items could also be weighted based o the cosequeces for slow or icorrect performace with the item. Coclusio This paper was about a geeral microecoomic method of usig a computatioal model of cogitio to compute the efficiecy of various decisios that occur durig practice. This work is relevat to educatio because it shows a ew approach to uderstadig how to improve educatio by cosiderig learig by the studet as the measure of profit. I this ew approach, the learig of sets of skills ca be optimized to imize output give iput. While we tied this method to a ACT-R cogitive model, there seems o reaso why this method could ot be used to optimize learig usig aother computatioal model. The elegace of the method explaied here is that it is theory eutral (give a particular model) ad so results i predictios that must be true give the limits of the particular model ad the accuracy of the utility fuctio used to capture prefereces. I practice, however, the potetial of this method ca be limited i domais where the complexity of the KC or LEs prevets the clear specificatio of a utility fuctio to optimize. Ackowledgmets This research was supported i part by grat from Roald Zdrojkowski for educatioal research; the Pittsburgh Sciece of Learig Ceter which is fuded by the Natioal Sciece Foudatio award umber SBE , ad a Graduate Traiig Grat awarded to Caregie Mello Uiversity by the Dept. of Educatio (#R305B040063). Refereces Aderso, J. R., Ficham, J. M., & Douglass, S. (997). The role of examples ad rules i the acquisitio of a cogitive skill. Joural of Experimetal Psychology: Learig, Memory, & Cogitio, 23(4), Aderso, J. R., & Lebiere, C. (998). The atomic compoets of thought. Mahwah, NJ, US: Lawrece Erlbaum Associates Publishers. Aderso, J. R., & Schooler, L. J. ( 99). Reflectios of the eviromet i memory. Psychological Sciece, 2(6), Ce, H., Koediger, K. R., & Juker, B. (2006). Learig factors aalysis - A geeral method for cogitive model evaluatio ad improvemet. I T.-W. Cha (Ed.), Lecture Notes i Computer Sciece Itelliget Tutorig Systems (Vol. 4053, pp ): Spriger. L oga, G. D. (995). The Weibull distributio, the power law, ad the istace theory of automaticity. Psychological Review, 02(4), M cclellad, J. L., McNaughto, B. L., & O'Reilly, R. C. (995). Why there are complemetary learig systems i the hippocampus ad eocortex: Isights from the successes ad failures of coectioist models of learig ad memory. Psychological Review, 02(3), Metcalfe, J., & Korell, N. (2003). The dyamics of learig ad allocatio of study time to a regio of proximal learig. Joural of Experimetal Psychology: Geeral, 32(4), Pa vlik Jr., P. I. (2005). The microecoomics of learig: Optimizig paired-associate memory. Dissertatio Abstracts Iteratioal: Sectio B: The Scieces ad Egieerig, 66(0-B), Pavlik Jr., P. I. (i press). Uderstadig ad applyig the dyamics of test practice ad study practice. Istructioal Sciece. Pavlik Jr., P. I., & Aderso, J. R. (2005). Practice ad forgettig effects o vocabulary memory: A activatiobased model of the spacig effect. Cogitive Sciece, 29(4), Pavlik Jr., P. I., Presso, N., Dozzi, G., Wu, S.-m., MacWhiey, B., & Koediger, K. R. (2007). The FaCT (Fact ad Cocept Traiig) System: A ew tool likig cogitive sciece with educators. I D. S. McNamara & J. G. Trafto (Eds.). Mahwah, NJ: Lawrece Erlbaum. Scheider, V. I., Healy, A. F., & Boure, L. E., Jr. (2002). What is leared uder difficult coditios is hard to forget: Cotextual iterferece effects i foreig vocabulary acquisitio, retetio, ad trasfer. Joural of Memory ad Laguage, 46(2), Vo Neuma, J., & Morgester, O. (944). Theory of games ad ecoomic behavior. Priceto,: Priceto uiversity press. 6
E-LEARNING USABILITY: A LEARNER-ADAPTED APPROACH BASED ON THE EVALUATION OF LEANER S PREFERENCES. Valentina Terzieva, Yuri Pavlov, Rumen Andreev
Titre du documet / Documet title E-learig usability : A learer-adapted approach based o the evaluatio of leaer's prefereces Auteur(s) / Author(s) TERZIEVA Valetia ; PAVLOV Yuri (1) ; ANDREEV Rume (2) ;
More informationFuzzy Reference Gain-Scheduling Approach as Intelligent Agents: FRGS Agent
Fuzzy Referece Gai-Schedulig Approach as Itelliget Agets: FRGS Aget J. E. ARAUJO * eresto@lit.ipe.br K. H. KIENITZ # kieitz@ita.br S. A. SANDRI sadra@lac.ipe.br J. D. S. da SILVA demisio@lac.ipe.br * Itegratio
More informationNatural language processing implementation on Romanian ChatBot
Proceedigs of the 9th WSEAS Iteratioal Coferece o SIMULATION, MODELLING AND OPTIMIZATION Natural laguage processig implemetatio o Romaia ChatBot RALF FABIAN, MARCU ALEXANDRU-NICOLAE Departmet for Iformatics
More informationManagement Science Letters
Maagemet Sciece Letters 4 (24) 2 26 Cotets lists available at GrowigSciece Maagemet Sciece Letters homepage: www.growigsciece.com/msl A applicatio of data evelopmet aalysis for measurig the relative efficiecy
More informationConsortium: North Carolina Community Colleges
Associatio of Research Libraries / Texas A&M Uiversity www.libqual.org Cotributors Collee Cook Texas A&M Uiversity Fred Heath Uiversity of Texas BruceThompso Texas A&M Uiversity Martha Kyrillidou Associatio
More information'Norwegian University of Science and Technology, Department of Computer and Information Science
The helpful Patiet Record System: Problem Orieted Ad Kowledge Based Elisabeth Bayega, MS' ad Samso Tu, MS2 'Norwegia Uiversity of Sciece ad Techology, Departmet of Computer ad Iformatio Sciece ad Departmet
More informationarxiv: v1 [cs.dl] 22 Dec 2016
ScieceWISE: Topic Modelig over Scietific Literature Networks arxiv:1612.07636v1 [cs.dl] 22 Dec 2016 A. Magalich, V. Gemmetto, D. Garlaschelli, A. Boyarsky Uiversity of Leide, The Netherlads {magalich,
More informationApplication for Admission
Applicatio for Admissio Admissio Office PO Box 2900 Illiois Wesleya Uiversity Bloomig, Illiois 61702-2900 Apply o-lie at: www.iwu.edu Applicatio Iformatio I am applyig: Early Actio Regular Decisio Early
More informationCONSTITUENT VOICE TECHNICAL NOTE 1 INTRODUCING Version 1.1, September 2014
preview begis oct 2014 lauches ja 2015 INTRODUCING WWW.FEEDBACKCOMMONS.ORG A serviced cloud platform to share ad compare feedback data ad collaboratively develop feedback ad learig practice CONSTITUENT
More informationHANDBOOK. Career Center Handbook. Tools & Tips for Career Search Success CALIFORNIA STATE UNIVERSITY, SACR AMENTO
HANDBOOK Career Ceter Hadbook CALIFORNIA STATE UNIVERSITY, SACR AMENTO Tools & Tips for Career Search Success Academic Advisig ad Career Ceter 6000 J Street Lasse Hall 1013 Sacrameto, CA 95819-6064 916-278-6231
More informationpart2 Participatory Processes
part part2 Participatory Processes Participatory Learig Approaches Whose Learig? Participatory learig is based o the priciple of ope expressio where all sectios of the commuity ad exteral stakeholders
More informationVISION, MISSION, VALUES, AND GOALS
6 VISION, MISSION, VALUES, AND GOALS 2010-2015 VISION STATEMENT Ohloe College will be kow throughout Califoria for our iclusiveess, iovatio, ad superior rates of studet success. MISSION STATEMENT The Missio
More information2014 Gold Award Winner SpecialParent
Award Wier SpecialParet Dedicated to all families of childre with special eeds 6 th Editio/Fall/Witer 2014 Desig ad Editorial Awards Competitio MISSION Our goal is to provide parets of childre with special
More informationOn March 15, 2016, Governor Rick Snyder. Continuing Medical Education Becomes Mandatory in Michigan. in this issue... 3 Great Lakes Veterinary
michiga veteriary medical associatio i this issue... 3 Great Lakes Veteriary Coferece 4 What You Need to Kow Whe Issuig a Iterstate Certificate of Ispectio 6 Low Pathogeic Avia Iflueza H5 Virus Detectios
More informationalso inside Continuing Education Alumni Authors College Events
SUMMER 2016 JAMESTOWN COMMUNITY COLLEGE ALUMNI MAGAZINE create a etrepreeur creatig a busiess a artist creatig beauty a citize creatig the future also iside Cotiuig Educatio Alumi Authors College Evets
More informationWhat s in a Step? Toward General, Abstract Representations of Tutoring System Log Data
What s in a Step? Toward General, Abstract Representations of Tutoring System Log Data Kurt VanLehn 1, Kenneth R. Koedinger 2, Alida Skogsholm 2, Adaeze Nwaigwe 2, Robert G.M. Hausmann 1, Anders Weinstein
More informationEffect of Word Complexity on L2 Vocabulary Learning
Effect of Word Complexity on L2 Vocabulary Learning Kevin Dela Rosa Language Technologies Institute Carnegie Mellon University 5000 Forbes Ave. Pittsburgh, PA kdelaros@cs.cmu.edu Maxine Eskenazi Language
More informationLanguage Acquisition Chart
Language Acquisition Chart This chart was designed to help teachers better understand the process of second language acquisition. Please use this chart as a resource for learning more about the way people
More informationProbabilistic Latent Semantic Analysis
Probabilistic Latent Semantic Analysis Thomas Hofmann Presentation by Ioannis Pavlopoulos & Andreas Damianou for the course of Data Mining & Exploration 1 Outline Latent Semantic Analysis o Need o Overview
More informationThe Good Judgment Project: A large scale test of different methods of combining expert predictions
The Good Judgment Project: A large scale test of different methods of combining expert predictions Lyle Ungar, Barb Mellors, Jon Baron, Phil Tetlock, Jaime Ramos, Sam Swift The University of Pennsylvania
More informationSeminar - Organic Computing
Seminar - Organic Computing Self-Organisation of OC-Systems Markus Franke 25.01.2006 Typeset by FoilTEX Timetable 1. Overview 2. Characteristics of SO-Systems 3. Concern with Nature 4. Design-Concepts
More informationThe Evolution of Random Phenomena
The Evolution of Random Phenomena A Look at Markov Chains Glen Wang glenw@uchicago.edu Splash! Chicago: Winter Cascade 2012 Lecture 1: What is Randomness? What is randomness? Can you think of some examples
More informationCal s Dinner Card Deals
Cal s Dinner Card Deals Overview: In this lesson students compare three linear functions in the context of Dinner Card Deals. Students are required to interpret a graph for each Dinner Card Deal to help
More informationCued Recall From Image and Sentence Memory: A Shift From Episodic to Identical Elements Representation
Journal of Experimental Psychology: Learning, Memory, and Cognition 2006, Vol. 32, No. 4, 734 748 Copyright 2006 by the American Psychological Association 0278-7393/06/$12.00 DOI: 10.1037/0278-7393.32.4.734
More informationProfessor Christina Romer. LECTURE 24 INFLATION AND THE RETURN OF OUTPUT TO POTENTIAL April 20, 2017
Economics 2 Spring 2017 Professor Christina Romer Professor David Romer LECTURE 24 INFLATION AND THE RETURN OF OUTPUT TO POTENTIAL April 20, 2017 I. OVERVIEW II. HOW OUTPUT RETURNS TO POTENTIAL A. Moving
More informationMath 181, Calculus I
Math 181, Calculus I [Semester] [Class meeting days/times] [Location] INSTRUCTOR INFORMATION: Name: Office location: Office hours: Mailbox: Phone: Email: Required Material and Access: Textbook: Stewart,
More informationEntrepreneurial Discovery and the Demmert/Klein Experiment: Additional Evidence from Germany
Entrepreneurial Discovery and the Demmert/Klein Experiment: Additional Evidence from Germany Jana Kitzmann and Dirk Schiereck, Endowed Chair for Banking and Finance, EUROPEAN BUSINESS SCHOOL, International
More informationModule 12. Machine Learning. Version 2 CSE IIT, Kharagpur
Module 12 Machine Learning 12.1 Instructional Objective The students should understand the concept of learning systems Students should learn about different aspects of a learning system Students should
More informationThe role of the first language in foreign language learning. Paul Nation. The role of the first language in foreign language learning
1 Article Title The role of the first language in foreign language learning Author Paul Nation Bio: Paul Nation teaches in the School of Linguistics and Applied Language Studies at Victoria University
More informationLecture 1: Machine Learning Basics
1/69 Lecture 1: Machine Learning Basics Ali Harakeh University of Waterloo WAVE Lab ali.harakeh@uwaterloo.ca May 1, 2017 2/69 Overview 1 Learning Algorithms 2 Capacity, Overfitting, and Underfitting 3
More informationHow to Judge the Quality of an Objective Classroom Test
How to Judge the Quality of an Objective Classroom Test Technical Bulletin #6 Evaluation and Examination Service The University of Iowa (319) 335-0356 HOW TO JUDGE THE QUALITY OF AN OBJECTIVE CLASSROOM
More informationMachine Learning and Data Mining. Ensembles of Learners. Prof. Alexander Ihler
Machine Learning and Data Mining Ensembles of Learners Prof. Alexander Ihler Ensemble methods Why learn one classifier when you can learn many? Ensemble: combine many predictors (Weighted) combina
More informationGCSE Mathematics B (Linear) Mark Scheme for November Component J567/04: Mathematics Paper 4 (Higher) General Certificate of Secondary Education
GCSE Mathematics B (Linear) Component J567/04: Mathematics Paper 4 (Higher) General Certificate of Secondary Education Mark Scheme for November 2014 Oxford Cambridge and RSA Examinations OCR (Oxford Cambridge
More informationLanguage Acquisition Fall 2010/Winter Lexical Categories. Afra Alishahi, Heiner Drenhaus
Language Acquisition Fall 2010/Winter 2011 Lexical Categories Afra Alishahi, Heiner Drenhaus Computational Linguistics and Phonetics Saarland University Children s Sensitivity to Lexical Categories Look,
More informationhave to be modeled) or isolated words. Output of the system is a grapheme-tophoneme conversion system which takes as its input the spelling of words,
A Language-Independent, Data-Oriented Architecture for Grapheme-to-Phoneme Conversion Walter Daelemans and Antal van den Bosch Proceedings ESCA-IEEE speech synthesis conference, New York, September 1994
More informationSOFTWARE EVALUATION TOOL
SOFTWARE EVALUATION TOOL Kyle Higgins Randall Boone University of Nevada Las Vegas rboone@unlv.nevada.edu Higgins@unlv.nevada.edu N.B. This form has not been fully validated and is still in development.
More informationNotes on The Sciences of the Artificial Adapted from a shorter document written for course (Deciding What to Design) 1
Notes on The Sciences of the Artificial Adapted from a shorter document written for course 17-652 (Deciding What to Design) 1 Ali Almossawi December 29, 2005 1 Introduction The Sciences of the Artificial
More informationOPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS
OPTIMIZATINON OF TRAINING SETS FOR HEBBIAN-LEARNING- BASED CLASSIFIERS Václav Kocian, Eva Volná, Michal Janošek, Martin Kotyrba University of Ostrava Department of Informatics and Computers Dvořákova 7,
More informationActivities, Exercises, Assignments Copyright 2009 Cem Kaner 1
Patterns of activities, iti exercises and assignments Workshop on Teaching Software Testing January 31, 2009 Cem Kaner, J.D., Ph.D. kaner@kaner.com Professor of Software Engineering Florida Institute of
More informationAP Calculus AB. Nevada Academic Standards that are assessable at the local level only.
Calculus AB Priority Keys Aligned with Nevada Standards MA I MI L S MA represents a Major content area. Any concept labeled MA is something of central importance to the entire class/curriculum; it is a
More informationReducing Features to Improve Bug Prediction
Reducing Features to Improve Bug Prediction Shivkumar Shivaji, E. James Whitehead, Jr., Ram Akella University of California Santa Cruz {shiv,ejw,ram}@soe.ucsc.edu Sunghun Kim Hong Kong University of Science
More informationMaximizing Learning Through Course Alignment and Experience with Different Types of Knowledge
Innov High Educ (2009) 34:93 103 DOI 10.1007/s10755-009-9095-2 Maximizing Learning Through Course Alignment and Experience with Different Types of Knowledge Phyllis Blumberg Published online: 3 February
More informationPredicting Students Performance with SimStudent: Learning Cognitive Skills from Observation
School of Computer Science Human-Computer Interaction Institute Carnegie Mellon University Year 2007 Predicting Students Performance with SimStudent: Learning Cognitive Skills from Observation Noboru Matsuda
More informationA New Perspective on Combining GMM and DNN Frameworks for Speaker Adaptation
A New Perspective on Combining GMM and DNN Frameworks for Speaker Adaptation SLSP-2016 October 11-12 Natalia Tomashenko 1,2,3 natalia.tomashenko@univ-lemans.fr Yuri Khokhlov 3 khokhlov@speechpro.com Yannick
More informationProbability and Statistics Curriculum Pacing Guide
Unit 1 Terms PS.SPMJ.3 PS.SPMJ.5 Plan and conduct a survey to answer a statistical question. Recognize how the plan addresses sampling technique, randomization, measurement of experimental error and methods
More informationConceptual and Procedural Knowledge of a Mathematics Problem: Their Measurement and Their Causal Interrelations
Conceptual and Procedural Knowledge of a Mathematics Problem: Their Measurement and Their Causal Interrelations Michael Schneider (mschneider@mpib-berlin.mpg.de) Elsbeth Stern (stern@mpib-berlin.mpg.de)
More informationLoughton School s curriculum evening. 28 th February 2017
Loughton School s curriculum evening 28 th February 2017 Aims of this session Share our approach to teaching writing, reading, SPaG and maths. Share resources, ideas and strategies to support children's
More informationMajor Milestones, Team Activities, and Individual Deliverables
Major Milestones, Team Activities, and Individual Deliverables Milestone #1: Team Semester Proposal Your team should write a proposal that describes project objectives, existing relevant technology, engineering
More informationMandarin Lexical Tone Recognition: The Gating Paradigm
Kansas Working Papers in Linguistics, Vol. 0 (008), p. 8 Abstract Mandarin Lexical Tone Recognition: The Gating Paradigm Yuwen Lai and Jie Zhang University of Kansas Research on spoken word recognition
More informationAge Effects on Syntactic Control in. Second Language Learning
Age Effects on Syntactic Control in Second Language Learning Miriam Tullgren Loyola University Chicago Abstract 1 This paper explores the effects of age on second language acquisition in adolescents, ages
More informationSpeech Recognition at ICSI: Broadcast News and beyond
Speech Recognition at ICSI: Broadcast News and beyond Dan Ellis International Computer Science Institute, Berkeley CA Outline 1 2 3 The DARPA Broadcast News task Aspects of ICSI
More informationVisual processing speed: effects of auditory input on
Developmental Science DOI: 10.1111/j.1467-7687.2007.00627.x REPORT Blackwell Publishing Ltd Visual processing speed: effects of auditory input on processing speed visual processing Christopher W. Robinson
More informationRule Learning With Negation: Issues Regarding Effectiveness
Rule Learning With Negation: Issues Regarding Effectiveness S. Chua, F. Coenen, G. Malcolm University of Liverpool Department of Computer Science, Ashton Building, Ashton Street, L69 3BX Liverpool, United
More informationCROSS-LANGUAGE INFORMATION RETRIEVAL USING PARAFAC2
1 CROSS-LANGUAGE INFORMATION RETRIEVAL USING PARAFAC2 Peter A. Chew, Brett W. Bader, Ahmed Abdelali Proceedings of the 13 th SIGKDD, 2007 Tiago Luís Outline 2 Cross-Language IR (CLIR) Latent Semantic Analysis
More informationFirms and Markets Saturdays Summer I 2014
PRELIMINARY DRAFT VERSION. SUBJECT TO CHANGE. Firms and Markets Saturdays Summer I 2014 Professor Thomas Pugel Office: Room 11-53 KMC E-mail: tpugel@stern.nyu.edu Tel: 212-998-0918 Fax: 212-995-4212 This
More informationContent Language Objectives (CLOs) August 2012, H. Butts & G. De Anda
Content Language Objectives (CLOs) Outcomes Identify the evolution of the CLO Identify the components of the CLO Understand how the CLO helps provide all students the opportunity to access the rigor of
More informationReinforcement Learning by Comparing Immediate Reward
Reinforcement Learning by Comparing Immediate Reward Punit Pandey DeepshikhaPandey Dr. Shishir Kumar Abstract This paper introduces an approach to Reinforcement Learning Algorithm by comparing their immediate
More informationCourse Name: Elementary Calculus Course Number: Math 2103 Semester: Fall Phone:
Course Name: Elementary Calculus Course Number: Math 2103 Semester: Fall 2011 Instructor s Name: Ricky Streight Hours Credit: 3 Phone: 405-945-6794 email: ricky.streight@okstate.edu 1. COURSE: Math 2103
More informationExtending Place Value with Whole Numbers to 1,000,000
Grade 4 Mathematics, Quarter 1, Unit 1.1 Extending Place Value with Whole Numbers to 1,000,000 Overview Number of Instructional Days: 10 (1 day = 45 minutes) Content to Be Learned Recognize that a digit
More informationAlgebra 1, Quarter 3, Unit 3.1. Line of Best Fit. Overview
Algebra 1, Quarter 3, Unit 3.1 Line of Best Fit Overview Number of instructional days 6 (1 day assessment) (1 day = 45 minutes) Content to be learned Analyze scatter plots and construct the line of best
More informationLecture 10: Reinforcement Learning
Lecture 1: Reinforcement Learning Cognitive Systems II - Machine Learning SS 25 Part III: Learning Programs and Strategies Q Learning, Dynamic Programming Lecture 1: Reinforcement Learning p. Motivation
More informationGrade 6: Correlated to AGS Basic Math Skills
Grade 6: Correlated to AGS Basic Math Skills Grade 6: Standard 1 Number Sense Students compare and order positive and negative integers, decimals, fractions, and mixed numbers. They find multiples and
More informationApplication of Virtual Instruments (VIs) for an enhanced learning environment
Application of Virtual Instruments (VIs) for an enhanced learning environment Philip Smyth, Dermot Brabazon, Eilish McLoughlin Schools of Mechanical and Physical Sciences Dublin City University Ireland
More informationA Process-Model Account of Task Interruption and Resumption: When Does Encoding of the Problem State Occur?
A Process-Model Account of Task Interruption and Resumption: When Does Encoding of the Problem State Occur? Dario D. Salvucci Drexel University Philadelphia, PA Christopher A. Monk George Mason University
More informationWeb-based Learning Systems From HTML To MOODLE A Case Study
Web-based Learning Systems From HTML To MOODLE A Case Study Mahmoud M. El-Khoul 1 and Samir A. El-Seoud 2 1 Faculty of Science, Helwan University, EGYPT. 2 Princess Sumaya University for Technology (PSUT),
More informationRadius STEM Readiness TM
Curriculum Guide Radius STEM Readiness TM While today s teens are surrounded by technology, we face a stark and imminent shortage of graduates pursuing careers in Science, Technology, Engineering, and
More informationArtificial Neural Networks written examination
1 (8) Institutionen för informationsteknologi Olle Gällmo Universitetsadjunkt Adress: Lägerhyddsvägen 2 Box 337 751 05 Uppsala Artificial Neural Networks written examination Monday, May 15, 2006 9 00-14
More informationStrategies for Solving Fraction Tasks and Their Link to Algebraic Thinking
Strategies for Solving Fraction Tasks and Their Link to Algebraic Thinking Catherine Pearn The University of Melbourne Max Stephens The University of Melbourne
More informationSoftware Maintenance
1 What is Software Maintenance? Software Maintenance is a very broad activity that includes error corrections, enhancements of capabilities, deletion of obsolete capabilities, and optimization. 2 Categories
More informationMathematics process categories
Mathematics process categories All of the UK curricula define multiple categories of mathematical proficiency that require students to be able to use and apply mathematics, beyond simple recall of facts
More informationOn Human Computer Interaction, HCI. Dr. Saif al Zahir Electrical and Computer Engineering Department UBC
On Human Computer Interaction, HCI Dr. Saif al Zahir Electrical and Computer Engineering Department UBC Human Computer Interaction HCI HCI is the study of people, computer technology, and the ways these
More informationCognitive Modeling. Tower of Hanoi: Description. Tower of Hanoi: The Task. Lecture 5: Models of Problem Solving. Frank Keller.
Cognitive Modeling Lecture 5: Models of Problem Solving Frank Keller School of Informatics University of Edinburgh keller@inf.ed.ac.uk January 22, 2008 1 2 3 4 Reading: Cooper (2002:Ch. 4). Frank Keller
More informationTo appear in The TESOL encyclopedia of ELT (Wiley-Blackwell) 1 RECASTING. Kazuya Saito. Birkbeck, University of London
To appear in The TESOL encyclopedia of ELT (Wiley-Blackwell) 1 RECASTING Kazuya Saito Birkbeck, University of London Abstract Among the many corrective feedback techniques at ESL/EFL teachers' disposal,
More informationLearning Methods in Multilingual Speech Recognition
Learning Methods in Multilingual Speech Recognition Hui Lin Department of Electrical Engineering University of Washington Seattle, WA 98125 linhui@u.washington.edu Li Deng, Jasha Droppo, Dong Yu, and Alex
More informationGuidelines for Project I Delivery and Assessment Department of Industrial and Mechanical Engineering Lebanese American University
Guidelines for Project I Delivery and Assessment Department of Industrial and Mechanical Engineering Lebanese American University Approved: July 6, 2009 Amended: July 28, 2009 Amended: October 30, 2009
More informationRunning head: DUAL MEMORY 1. A Dual Memory Theory of the Testing Effect. Timothy C. Rickard. Steven C. Pan. University of California, San Diego
Running head: DUAL MEMORY 1 A Dual Memory Theory of the Testing Effect Timothy C. Rickard Steven C. Pan University of California, San Diego Word Count: 14,800 (main text and references) This manuscript
More informationEdexcel GCSE. Statistics 1389 Paper 1H. June Mark Scheme. Statistics Edexcel GCSE
Edexcel GCSE Statistics 1389 Paper 1H June 2007 Mark Scheme Edexcel GCSE Statistics 1389 NOTES ON MARKING PRINCIPLES 1 Types of mark M marks: method marks A marks: accuracy marks B marks: unconditional
More informationDIDACTIC MODEL BRIDGING A CONCEPT WITH PHENOMENA
DIDACTIC MODEL BRIDGING A CONCEPT WITH PHENOMENA Beba Shternberg, Center for Educational Technology, Israel Michal Yerushalmy University of Haifa, Israel The article focuses on a specific method of constructing
More informationDERMATOLOGY. Sponsored by the NYU Post-Graduate Medical School. 129 Years of Continuing Medical Education
Advaces i DERMATOLOGY THURSDAY - FRIDAY JUNE 7-8, 2012 New York, NY Sposored by the NYU Post-Graduate Medical School 129 Years of Cotiuig Medical Educatio THE RONALD O. PERELMAN DEPARTMENT OF DERMATOLOGY
More informationNew Venture Financing
New Venture Financing General Course Information: FINC-GB.3373.01-F2017 NEW VENTURE FINANCING Tuesdays/Thursday 1.30-2.50pm Room: TBC Course Overview and Objectives This is a capstone course focusing on
More informationA Reinforcement Learning Variant for Control Scheduling
A Reinforcement Learning Variant for Control Scheduling Aloke Guha Honeywell Sensor and System Development Center 3660 Technology Drive Minneapolis MN 55417 Abstract We present an algorithm based on reinforcement
More informationInstructor: Matthew Wickes Kilgore Office: ES 310
MATH 1314 College Algebra Syllabus Instructor: Matthew Wickes Kilgore Office: ES 310 Longview Office: LN 205C Email: mwickes@kilgore.edu Phone: 903 988-7455 Prerequistes: Placement test score on TSI or
More informationThe Perception of Nasalized Vowels in American English: An Investigation of On-line Use of Vowel Nasalization in Lexical Access
The Perception of Nasalized Vowels in American English: An Investigation of On-line Use of Vowel Nasalization in Lexical Access Joyce McDonough 1, Heike Lenhert-LeHouiller 1, Neil Bardhan 2 1 Linguistics
More informationAn Empirical and Computational Test of Linguistic Relativity
An Empirical and Computational Test of Linguistic Relativity Kathleen M. Eberhard* (eberhard.1@nd.edu) Matthias Scheutz** (mscheutz@cse.nd.edu) Michael Heilman** (mheilman@nd.edu) *Department of Psychology,
More informationThe Effectiveness of Realistic Mathematics Education Approach on Ability of Students Mathematical Concept Understanding
International Journal of Sciences: Basic and Applied Research (IJSBAR) ISSN 2307-4531 (Print & Online) http://gssrr.org/index.php?journal=journalofbasicandapplied ---------------------------------------------------------------------------------------------------------------------------
More informationLearning From the Past with Experiment Databases
Learning From the Past with Experiment Databases Joaquin Vanschoren 1, Bernhard Pfahringer 2, and Geoff Holmes 2 1 Computer Science Dept., K.U.Leuven, Leuven, Belgium 2 Computer Science Dept., University
More informationA GENERIC SPLIT PROCESS MODEL FOR ASSET MANAGEMENT DECISION-MAKING
A GENERIC SPLIT PROCESS MODEL FOR ASSET MANAGEMENT DECISION-MAKING Yong Sun, a * Colin Fidge b and Lin Ma a a CRC for Integrated Engineering Asset Management, School of Engineering Systems, Queensland
More information4.0 CAPACITY AND UTILIZATION
4.0 CAPACITY AND UTILIZATION The capacity of a school building is driven by four main factors: (1) the physical size of the instructional spaces, (2) the class size limits, (3) the schedule of uses, and
More informationarxiv: v1 [cs.cl] 2 Apr 2017
Word-Alignment-Based Segment-Level Machine Translation Evaluation using Word Embeddings Junki Matsuo and Mamoru Komachi Graduate School of System Design, Tokyo Metropolitan University, Japan matsuo-junki@ed.tmu.ac.jp,
More informationDesigning a Rubric to Assess the Modelling Phase of Student Design Projects in Upper Year Engineering Courses
Designing a Rubric to Assess the Modelling Phase of Student Design Projects in Upper Year Engineering Courses Thomas F.C. Woodhall Masters Candidate in Civil Engineering Queen s University at Kingston,
More informationCross Language Information Retrieval
Cross Language Information Retrieval RAFFAELLA BERNARDI UNIVERSITÀ DEGLI STUDI DI TRENTO P.ZZA VENEZIA, ROOM: 2.05, E-MAIL: BERNARDI@DISI.UNITN.IT Contents 1 Acknowledgment.............................................
More informationExploration. CS : Deep Reinforcement Learning Sergey Levine
Exploration CS 294-112: Deep Reinforcement Learning Sergey Levine Class Notes 1. Homework 4 due on Wednesday 2. Project proposal feedback sent Today s Lecture 1. What is exploration? Why is it a problem?
More informationPhysics 270: Experimental Physics
2017 edition Lab Manual Physics 270 3 Physics 270: Experimental Physics Lecture: Lab: Instructor: Office: Email: Tuesdays, 2 3:50 PM Thursdays, 2 4:50 PM Dr. Uttam Manna 313C Moulton Hall umanna@ilstu.edu
More informationDeveloping a Language for Assessing Creativity: a taxonomy to support student learning and assessment
Investigations in university teaching and learning vol. 5 (1) autumn 2008 ISSN 1740-5106 Developing a Language for Assessing Creativity: a taxonomy to support student learning and assessment Janette Harris
More information2.B.4 Balancing Crane. The Engineering Design Process in the classroom. Summary
2.B.4 Balancing Crane The Engineering Design Process in the classroom Grade Level 2 Sessions 1 40 minutes 2 30 minutes Seasonality None Instructional Mode(s) Whole class, groups of 4 5 students, individual
More informationCS Machine Learning
CS 478 - Machine Learning Projects Data Representation Basic testing and evaluation schemes CS 478 Data and Testing 1 Programming Issues l Program in any platform you want l Realize that you will be doing
More informationUtilizing Soft System Methodology to Increase Productivity of Shell Fabrication Sushant Sudheer Takekar 1 Dr. D.N. Raut 2
IJSRD - International Journal for Scientific Research & Development Vol. 2, Issue 04, 2014 ISSN (online): 2321-0613 Utilizing Soft System Methodology to Increase Productivity of Shell Fabrication Sushant
More informationCHAPTER 4: REIMBURSEMENT STRATEGIES 24
CHAPTER 4: REIMBURSEMENT STRATEGIES 24 INTRODUCTION Once state level policymakers have decided to implement and pay for CSR, one issue they face is simply how to calculate the reimbursements to districts
More informationStudent Morningness-Eveningness Type and Performance: Does Class Timing Matter?
Student Morningness-Eveningness Type and Performance: Does Class Timing Matter? Abstract Circadian rhythms have often been linked to people s performance outcomes, although this link has not been examined
More informationThe Netherlands. Jeroen Huisman. Introduction
4 The Netherlands Jeroen Huisman Introduction Looking solely at the legislation, one could claim that the Dutch higher education system has been officially known as a binary system since 1986. At that
More information