Linked Markov sources: Modeling outcome-dependent social processes

Many social processes are adaptive in the sense that the process changes as a result of previous outcomes. Data on such processes may come in the form of categorical time series. First, the authors propose a class of Markov Source models that embody such adaptation. Second, the authors discuss new methods to evaluate the fit of such models. Third, the authors apply these models and methods to data on a social process that is a preeminent example of an adaptive process: (encoded) conversation as arises in structured interviews. © 2007 Sage Publications

Understanding novice programmer behavior on introductory courses - Learning analytics approach

It is not easy to learn programming. This is why increasing theoretical and practical knowledge in programming education benefits both the educators as well as the students. To allow the students to gain maximal benefit from their studies, the educator must be able to recognize the students who are struggling with learning programming. Learning analytics provides a possible solution to this problem. This thesis demonstrates a novel method to model programmer behavior by using Markov Models. Programming fulfills the Markov property, because the success of the next attempt to compile or execute code is not influenced by the previous attempts; only by the current skill level of the programmer. The model is built using a state machine, which consists of states representing the different phases of the programming process. The state machine contains eight different states and 29different state transition possibilities. A Markov chain corresponding to a specific student can be computed using this state machine and then used with, for example machine learning algorithms. The data for this thesis was collected from a total of five different introductory programming courses, which used either the Java or Python programming languages. The dataset contains 1174 unique students, who made 544 835 total submissions to411 unique assignments. All programming courses were given in Turku, during2017-2021.This thesis provides a theoretical basis for modeling students (Markov Models) and offers a practical method to model students using Markov Models. This thesis only applies unsupervised machine learning methods to the data, specifically the K-Means clustering algorithm. However, supervised methods may also be used. The usefulness of the model is demonstrated by clustering students into three statistically similar clusters: students who perform well, average and poorly. The model is also applied to recognize the programming language used, based only on the transitions within the state machine.--- Ohjelmoinnin oppiminen ei ole helppoa. Tästä syystä ohjelmoinnin opetuksenteoreettinen ja käytännön edistäminen hyödyttää paitsi nykyisin ohjelmointia opettavia, myös opiskelijoita. Jotta opiskelijat voivat saavuttaa maksimaalisenhyödyn opiskelustaan, opettajan täytyy voida tunnistaa ne opiskelijat, joille ohjelmoinnin opiskelu tuottaa hankaluuksia. Oppimisanalytiikka tarjoaa tähän mahdollisuuden. Tämä väitöskirja esittelee tavan mallintaa ohjelmoinnin opiskelijoidenkäyttäytymistä käyttämällä Markovin malleja. Ohjelmoijan käyttäytyminen toteuttaa Markovin ominaisuuden, sillä ohjelmoijan koodin ajoyrityksen onnistumiseen vaikuttaa ainoastaan ohjelmoijan senhetkinen taitotaso; aikaisemmilla yrityksillä ei ole vaikutusta tuleviin kertoihin. Malli rakennetaan käyttämällä tilakonetta, jonka jokainen tila vastaa ohjelmointiprosessin vaihetta. Tilakoneessa on yhteensä kahdeksan eri tilaa ja 29 erilaista tilan muutosmahdollisuutta. Tilakoneesta lasketaan opiskelijaa vastaava Markovin ketju, mitä voidaan käyttää esimerkiksi koneoppimisalgoritmien kanssa. Dataa tähän väitöskirjaan kerättiin yhteensä viidestä ohjelmoinninperuskurssista, joissa käytettiin joko Java- tai Python-ohjelmointikieltä. Opiskelijoita kursseilla oli yhteensä 1174. Opiskelijat tekivät yhteensä 544-835 ohjelmointitehtävän palautusta 411 ohjelmointitehtävään. Kaikki ohjelmointikurssit pidettiin Turussa, vuosina 2017-2021 Tämä väitöskirja tarjoaa teoreettisen pohjan ohjelmoinnin opiskelijoidenmallintamiseen (Markovin mallit) ja tarjoaa menetelmän, jolla Markovin malleja käyttämällä voi mallintaa ohjelmoinnin opiskelijoita. Malliin sovelletaan vain ohjaamattomia koneoppimismenetelmiä, erityisesti K-Means clustering -algoritmia. Tässä väitöskirjassa osoitan myös teoreettisen mallin muutamia käytännönsovelluksia luokittelemalla opiskelijoita samoja ominaisuuksia sisältäviin luokkiin. Malli opetetaan erottelemaan opiskelijat kolmeen ryhmään: hyvin, keskiverrosti ja huonosti pärjääviin. Mallia sovelletaan onnistuneesti myös tunnistamaan käytetty ohjelmointikieli käyttämällä vain tilakoneen tilasiirtymiä

MOCAST 2021

The 10th International Conference on Modern Circuit and System Technologies on Electronics and Communications (MOCAST 2021) will take place in Thessaloniki, Greece, from July 5th to July 7th, 2021. The MOCAST technical program includes all aspects of circuit and system technologies, from modeling to design, verification, implementation, and application. This Special Issue presents extended versions of top-ranking papers in the conference. The topics of MOCAST include:Analog/RF and mixed signal circuits;Digital circuits and systems design;Nonlinear circuits and systems;Device and circuit modeling;High-performance embedded systems;Systems and applications;Sensors and systems;Machine learning and AI applications;Communication; Network systems;Power management;Imagers, MEMS, medical, and displays;Radiation front ends (nuclear and space application);Education in circuits, systems, and communications

Beyond Standard Assumptions - Semiparametric Models, A Dyadic Item Response Theory Model, and Cluster-Endogenous Random Intercept Models

In most statistical analyses, quantitative education researchers often make simplifying assumptions regarding the manner in which their data was generated in order to answer some of these questions. These assumptions can help to reduce the complexity of the problem, and allow the researcher to describe their data using a simpler, and often times more interpretable, statistical model. However, making some of these assumptions when they are not true can lead to biased estimates and misleading answers. While the standard sets of assumptions associated with commonly-used statistical models are usually sufficient in a wide range of contexts, it will always be beneficial for education researchers to understand what they are, when they are reasonable, and how to modify them if necessary. This dissertation focuses on three of the most common models used in quantitative education research (viz. parametric models like Linear Models (LMs), Item Response Theory (IRT) models, and Random-Intercept Models (RIMs)), discusses the standard sets of assumptions that accompany these models, and then describes related models with less stringent sets of assumptions. In each of the following three chapters, we either explicitly unpack existing models that are useful but are currently still uncommon in the field of education research, or propose novel models and/or estimation strategies for these models. We begin in Chapter 1 with a common parametric model known as the Gaussian LM, and use it as a scaffold to better understand semiparametric models and their estimation. We begin by reviewing how the coefficients of the Gaussian LM are usually estimated using Maximum Likelihood (ML) or Least-Squares (LS). We then introduce the notion of an $m$-estimator as well as that of a Regular Asymptotically Linear estimator, and show how they relate to the ML estimator. In particular, we introduce the notion of influence functions/curves and discuss their geometry together with concepts such as Hilbert spaces and tangent spaces. We then demonstrate, concretely, how to derive the so-called efficient influence function under the Gaussian LM, and show that it is precisely the influence function of the ML and (Ordinary) LS estimators. This shows that the ML estimator (at least under the Gaussian LM) is efficient. Using the foundation built, we move on from the Gaussian LM by relaxing both the assumption that the residuals are normally distributed, as well as the assumption that they have a constant variance, and define this as the Heteroskedastic Linear Model. Unlike the Gaussian LM, this is a semiparametric model. Where possible, we make use of intuition and analogous results from the parametric setting to help describe the workflow for obtaining an efficient estimator for the coefficients of the Heteroskedastic Linear Model. In particular, we derive the nuisance tangent space for this semiparametric model, and use it to obtain the efficient influence function for our model. We then show how to use the efficient influence function to obtain an efficient estimator (which happens to be the Weighted LS estimator) from the (Ordinary) LS estimator via a one-step approach as well as an estimating equations approach. We then conclude by directing readers to more advanced material, including references on more modern approaches to estimating more general semiparametric models such as Targeted Maximum Likelihood Estimation. In Chapter 2, we focus on a class of measurement models known as Item Response Theory models which are useful for measuring latent traits of a subject based on the subject's response to items. We relax the condition that the responses are only a result of the individual's latent trait (and possibly an external rater), and propose a dyadic Item Response Theory (dIRT) model for measuring interactions of pairs of individuals when the responses to items represent the actions (or behaviors, perceptions, etc.) of each individual (actor) made within the context of a dyad formed with another individual (partner). Examples of its use in education include the assessment of collaborative problem solving among students, or the evaluation of intra-departmental dynamics among teachers. The dIRT model generalizes both Item Response Theory models for measurement and the Social Relations Model for dyadic data. Here, the responses of an actor when paired with a partner are modeled as a function of not only the actor's inclination to act and the partner's tendency to elicit that action, but also the unique relationship of the pair, represented by two directional, possibly correlated, interaction latent variables. We discuss generalizations such as accommodating triads or larger groups, but focus on demonstrating the key idea in the dyadic case. We show that estimation may be performed using Markov-chain Monte Carlo implemented in \texttt{Stan}, making it straightforward to extend the dIRT model in various ways. Specifically, we show how the basic dIRT model can be extended to accommodate latent regressions, random effects, distal outcomes. We perform a simulation study that demonstrates that our estimation approach performs well. In the absence of educational data of this form, we demonstrate the usefulness of our proposed approach using speed-dating data instead, and find new evidence of pairwise interactions between participants, describing a mutual attraction that is inadequately characterized by individual properties alone.Finally, in Chapter 3, we consider the often implicit assumption made when estimating the coefficients of structural Random Intercept Models (RIMs) that covariates at all levels do not co-vary with the random intercepts. A violation of this assumption (called cluster-level endogeneity) leads to inconsistent estimates when using standard estimation procedures. For two-level RIMs with such endogeneity, Hausman and Taylor (HT) devised a consistent multi-step instrumental variable estimator using only internal instruments. We, instead, approach this problem by explicitly modeling the endogeneity using a Structural Equation Model (SEM). In this chapter, we compare, through simulation, the HT and SEM estimators, and evaluate their asymptotic and finite sample properties. We show that the SEM approach is also flexible enough to deal with different exchangeability assumptions for the covariates (e.g., whether the correlations between pairs of all units in a cluster are the same) and investigate how these exchangeability assumptions affect finite sample properties of the HT estimator. For the simulations, we propose a new procedure for generating cluster- and unit-level covariates and random intercepts with a fully flexible covariance structure. We also compare our approach to another common approach known as Multilevel Matching using data from the High School and Beyond survey

Probabilistic Modelling of Morphologically Rich Languages

This thesis investigates how the sub-structure of words can be accounted for in probabilistic models of language. Such models play an important role in natural language processing tasks such as translation or speech recognition, but often rely on the simplistic assumption that words are opaque symbols. This assumption does not fit morphologically complex language well, where words can have rich internal structure and sub-word elements are shared across distinct word forms. Our approach is to encode basic notions of morphology into the assumptions of three different types of language models, with the intention that leveraging shared sub-word structure can improve model performance and help overcome data sparsity that arises from morphological processes. In the context of n-gram language modelling, we formulate a new Bayesian model that relies on the decomposition of compound words to attain better smoothing, and we develop a new distributed language model that learns vector representations of morphemes and leverages them to link together morphologically related words. In both cases, we show that accounting for word sub-structure improves the models' intrinsic performance and provides benefits when applied to other tasks, including machine translation. We then shift the focus beyond the modelling of word sequences and consider models that automatically learn what the sub-word elements of a given language are, given an unannotated list of words. We formulate a novel model that can learn discontiguous morphemes in addition to the more conventional contiguous morphemes that most previous models are limited to. This approach is demonstrated on Semitic languages, and we find that modelling discontiguous sub-word structures leads to improvements in the task of segmenting words into their contiguous morphemes.Comment: DPhil thesis, University of Oxford, submitted and accepted 2014. http://ora.ox.ac.uk/objects/uuid:8df7324f-d3b8-47a1-8b0b-3a6feb5f45c

Hidden Markov Models

Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research

Dynamics of Social Networks: Multi-agent Information Fusion, Anticipatory Decision Making and Polling

This paper surveys mathematical models, structural results and algorithms in controlled sensing with social learning in social networks. Part 1, namely Bayesian Social Learning with Controlled Sensing addresses the following questions: How does risk averse behavior in social learning affect quickest change detection? How can information fusion be priced? How is the convergence rate of state estimation affected by social learning? The aim is to develop and extend structural results in stochastic control and Bayesian estimation to answer these questions. Such structural results yield fundamental bounds on the optimal performance, give insight into what parameters affect the optimal policies, and yield computationally efficient algorithms. Part 2, namely, Multi-agent Information Fusion with Behavioral Economics Constraints generalizes Part 1. The agents exhibit sophisticated decision making in a behavioral economics sense; namely the agents make anticipatory decisions (thus the decision strategies are time inconsistent and interpreted as subgame Bayesian Nash equilibria). Part 3, namely {\em Interactive Sensing in Large Networks}, addresses the following questions: How to track the degree distribution of an infinite random graph with dynamics (via a stochastic approximation on a Hilbert space)? How can the infected degree distribution of a Markov modulated power law network and its mean field dynamics be tracked via Bayesian filtering given incomplete information obtained by sampling the network? We also briefly discuss how the glass ceiling effect emerges in social networks. Part 4, namely \emph{Efficient Network Polling} deals with polling in large scale social networks. In such networks, only a fraction of nodes can be polled to determine their decisions. Which nodes should be polled to achieve a statistically accurate estimates

Selecting an Optimal Measurement Model and Detecting Differential Item Functioning Using Bayesian Confirmatory Factor Analysis

I investigated the sampling behavior of DIC and WAIC in the context of selecting an optimal measurement model in Bayesian SEM, as well as the utility of highly constrained parameter estimates in detecting differential item functioning (DIF). I assessed the relative efficiency of WAIC compared to DIC, evaluated analytical WAIC SEs by calculating relative bias, and reported how often WAIC and DIC indicated a preference for each invariance model. I compared the power and Type I error rates for DIF detection across conditions, and assessed the quality of estimates by calculating bias and 95% CI coverage rates for key parameters. Results indicate that although WAIC has less sampling variability than DIC, their model preferences are similar. Both WAIC and DIC have greater power to detect that invariance constraints are untenable than AIC in using maximum likelihood (ML) estimation. In tests of null hypotheses that DIF parameters are zero, Bayesian credible intervals and ML modification indices have similar power, but Bayesian credible intervals have much lower Type I error rates