Exploratory sequential data analysis of user interaction in contemporary BIM applications

Creation oriented software allows the user to work according to their own vision and rules. From the perspective of software analysis, this is challenging because there is no certainty as to how the users are using the software and what kinds of workflows emerge among different users. The aim of this thesis was to study and identify the potential of sequential event pattern data extraction analysis from expert field creation oriented software in the field of Building Information Modeling (BIM). The thesis additionally introduces a concept evaluation model for detecting repetition based usability disruption. Finally, the work presents an implementation of sequential pattern mining based user behaviour analysis and machine learning predictive application using state of the art algorithms. The thesis introduces a data analysis implementation that is built upon inspections of Sequential or Exploratory Sequential Data Analysis (SDA or ESDA) based theory in usability studies. The study implements a test application specific workflow sequence detection and database transfer approach. The paper uses comparative modern mining algorithms known as BIDE and TKS for sequential pattern discovery. Finally, the thesis utilizes the created sequence database to create user detailing workflow predictions using a CPT+ algorithm. The main contribution of the thesis outcome is to open scalable options for both software usability and product development to automatically recognize and predict usability and workflow related information, deficiencies and repetitive workflow. By doing this, more quantifiable metrics can be revealed in relation to software user interface behavior analytics.Luomiseen perustuva ohjelmisto mahdollistaa käyttäjän työskentelyn oman visionsa ja sääntöjensä mukaisesti. Ohjelmien analysoinnin kannalta tämä on haastavaa, koska ei ole varmuutta siitä, kuinka ohjelmistoa tarkalleen käytetään ja millaisia työskentelytapoja ohjelmiston eri käyttäjäryhmille voi syntyä. Opinnäytetyön tavoitteena oli tutkia ja identifioida toistuvien käyttäjätapahtumasekvenssien analyysipotentiaalia tietomallinnukseen keskittyvässä luomispoh jaisessa ohjelmistossa. Opinnäyte esittelee myös evaluointimallikonseptin, jonka avulla on mahdollista tunnistaa toistuvasta käyttäytymisestä aiheutuvia käytettävyysongelmia. Lopuksi työssä esitellään sekvenssianalyysiin perustuva ohjelmiston käyttäjän toiminta-analyysi sekä ennustava koneoppimisen sovellus. Opinnäytetyössä esitellään data-analyysisovellus, joka perustuu käytettävyystutkimuksessa esiintyvien toistuvien sekvenssien tai kokeellisesti toistuvien sekvenssien analyysiteorian tarkasteluun. Sovelluksen toteutus on tehty eritoten työssä käytetylle ohjelmistolle, jossa käyttäjän detaljointitapahtumista muodostetaan sekvenssejä sekvenssitietokannan luomiseksi. Työssä käytetään sekvenssien toistuvuusanalyysiin moderneja louhintamenetelmiä nimeltään BIDE ja TKS. Lopuksi työssä hyödynnetään luotua sekvenssitietokantaa myös käyttäjän detaljointityön ennustamista varten käyttämällä CPT+ algoritmia. Opinnäytetyön tulosten pohjalta pyritään löytämään vaihtoehtoja käytettävyyden ja tuotekehityksen päätöksenteon tietopohjaiseksi tueksi tunnistamalla ja ennusta malla käyttäjien toimintaa ohjelmistossa. Löydetyn informaation avulla on mahdollista ilmaista käytettävyyteen liittyviä ongelmia kvantitatiivisen tiedon valossa

Diagnostic Classification Modeling of Rubric-Scored Constructed-Response Items

The need for formative assessments has led to the development of a psychometric framework known as diagnostic classification models (DCMs), which are mathematical measurement models designed to estimate the possession or mastery of a designated set of skills or attributes within a chosen construct. Furthermore, much research has gone into the practice of “retrofitting” diagnostic measurement models to existing assessments in order to improve their diagnostic capability. Although retrofitting DCMs to existing assessments can theoretically improve diagnostic potential, it is also prone to challenges including identifying multidimensional traits from largely unidimensional assessments, a lack of assessments that are suitable for the DCM framework, and statistical quality, specifically highly correlated attributes and poor model fit. Another recent trend in assessment has been a move towards creating more authentic constructed-response assessments. For such assessments, rubric-based scoring is often seen as method of providing reliable scoring and interpretive formative feedback. However, rubric-scored tests are limited in their diagnostic potential in that they are usually used to assign unidimensional numeric scores. It is the purpose of this thesis to propose general methods for retrofitting DCMs to rubric-scored assessments. Two methods will be proposed and compared: (1) automatic construction of an attribute hierarchy to represent all possible numeric score levels from a rubric-scored assessment and (2) using rubric criterion score level descriptions to imply an attribute hierarchy. This dissertation will describe these methods, discuss the technical and mathematical issues that arise in using them, and apply and compare both methods to a prominent rubric-scored test of critical thinking skills, the Collegiate Learning Assessment+ (CLA+). Finally, the utility of the proposed methods will be compared to a reasonable alternative methodology: the use of polytomous IRT models, including the Graded Response Model (GRM), the Partial Credit Model (PCM), and the Generalized-Partial Credit Model (G-PCM), for this type of test score data

NEURAL NETWORKS FOR DECISION SUPPORT: PROBLEMS AND OPPORTUNITIES

Neural networks offer an approach to computing which - unlike conventional programming - does not necessitate a complete algorithmic specification. Furthermore, neural networks provide inductive means for gathering, storing, and using, experiential knowledge. Incidentally, these have also been some of the fundamental motivations for the development of decision support systems in general. Thus, the interest in neural networks for decision support is immediate and obvious. In this paper, we analyze the potential contribution of neural networks for decision support, on one hand, and point out at some inherent constraints that might inhibit their use, on the other. For the sake of completeness and organization, the analysis is carried out in the context of a general-purpose DSS framework that examines all the key factors that come into play in the design of any decision support system.Information Systems Working Papers Serie

Doctor of Philosophy

dissertationEarly identification and intervention of an Autism Spectrum Disorder (ASD) can have beneficial effects that extend into later life. However, currently used instruments have difficulties detecting children who may have an ASD. The current study investigated the utility of a newly published measure, Autism Spectrum Rating Scales (ASRS). Participants included 67 children ages 2 to 5 years old, referred for possible special education services. Participants were divided into two groups: those with an ASD (n = 37) and others suspected of having a general developmental disability (DD) (n = 30). Participants were assessed using the ASRS to examine the instrument's ability to classify them as having an ASD or a general DD. Additional testing examined the effects various levels of intellectual, adaptive, and language skills have on the ability of the ASRS to classify children. Classification ability and error rates of the ASRS were also examined with regard to base rates and error acceptability by context. Results indicate that with a recommended cut score of 70, the Parent ASRS had an overall hit rate of 64%. The Parent ASRS had a Type I error rate (i.e., false positive) of 16% and a Type II error rate (i.e., false negative) of 19%. For the Teacher ASRS, the hit rate was 62%. The Teacher ASRS had a Type I error rate of 15% and a Type II error rate of 23%. Sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio and negative likelihood ratio of the ASRS were also examined to gain insight into the measure's utility. ROC Curve analysis determined the area under the curve (AUC) for the ASRS, the most optimal point for sensitivity and specificity. It was concluded that across all ASRS forms (e.g., Parent, Teacher), the general ability of the ASRS to classify and discriminate between children with potential ASDs or general DDs referred for possible special education services were similar

Using movement kinematics to understand the motor side of Autism Spectrum Disorder

openComprensione del sintomo motorio dell'autismo attraverso la cinematica del movimentoBeside core deficits in social interaction and communication, atypical motor patterns have been often reported in people with Autism Spectrum Disorder (ASD). It has been recently speculated that a part of these sensorimotor abnormalities could be better explained considering prospective motor control (i.e., the ability to plan actions toward future events or consider future task demands), which has been hypothesized to be crucial for higher mind functions (e.g., understand intentions of other people) (Trevarthen and Delafield-Butt 2013). The aim of the current dissertation was to tackle the motor ‘side’ in ASD exploring whether and how prospective motor control might be atypical in children with a diagnosis of autism, given that actions are directed into the future and their control is based on knowledge of what is going to happen next (von Hofsten and Rosander 2012). To do this, an integrative approach based on neuropsychological assessment, behavioural paradigms and machine learning modelling of the kinematics recorded with motion capture techniques was applied in typically developing children and children with ASD without accompanying intellectual impairment.openXXXI CICLO - ARCHITETTURA E DESIGN - Design navale e nauticoBECCHIO, CRISTINA (IIT)Podda, Jessic

Discrete Roles for Impulsivity and Compulsivity in Gambling Disorder

Background and Objective: Complex associations between gambling disorder (GD) and impulsivity have been identified. However, little is known regarding how compulsivity associates with different impulsivity domains in GD. In this study, we examined associations between self-reported and behavioral measures of impulsivity-assessed through the Barratt Impulsiveness Scale (BIS-11) and the Experiential Discounting Task (EDT), respectively- and compulsivity-measured using the Padua Inventory and the Wisconsin Card Sorting Test (WCST), respectively-, in an adult sample with GD (N = 132, 94 men and 38 women, ages ranging from 18 to 69 years). GD severity was assessed using the South Oaks Gambling Screen. Methods: Structural Equation Modeling was used to examine relationships between impulsivity and compulsivity measures, age, and GD severity. Results: BIS-11 non-planning and BIS-11 total scores positively correlated with GD severity. The standardized coefficients for the SEM showed direct positive contributions of BIS-11 non-planning, Padua and EDT scores to GD severity. Only participants' ages directly contributed to WCST perseverative errors, and no direct or indirect effects were found with respect to GD severity. Conclusion: The findings suggest that specific aspects of impulsivity and compulsivity contribute to GD severity. Interventions specifically targeting domains that are most relevant to GD severity may improve treatment outcomes

An integrated study of earth resources in the State of California based on ERTS-1 and supporting aircraft data, volume 1

There are no author-identified significant results in this report