A Rule-Based Expert System for Teachers’ Certification in the Use of Learning Management Systems

In recent years and accelerated by the arrival of the COVID-19 pandemic, Learning Management Systems (LMS) are increasingly used as a complement to university teaching. LMS provide an important number of resources and activities that teachers can freely select to complement their teaching, which means courses with different usage patterns difficult to characterize. This study proposes an expert system to automatically classify courses and certify teachers’ LMS competence from LMS logs. The proposed system uses clustering to stablish the classification scheme. From the output of this algorithm, it defines the rules used to classify courses. Data registered from a university virtual campus with 3,303 courses and two million interactive events have been used to obtain the classification scheme and rules. The system has been validated against a group of experts. Results show that it performs successfully. Therefore, it can be concluded that the system can automatically and satisfactorily evaluate and certify the teachers’ LMS competence evidenced in their courses

Contributions to IT project portfolio management and individual digital study assistants in higher education

Diese kumulative Dissertation beschreibt und diskutiert 12 wissenschaftliche Artikel, die einen Beitrag zur Forschung in den Themenbereichen Informationstechnik (IT) Projektportfoliomanagement und individuelle, digitale Studienassistenten an Universitäten umfasst. Dafür wurden Modelle und Rahmenwerke entwickelt, die wesentliche IT Projektportfoliomanagement Phasen und Aktivitäten beschreiben, eine objektive IT Projektevaluation ermöglichen und Reifegrade von IT Projektportfoliomanagement Prozessen bestimmen. Außerdem wurde ein Optimierungsmodell zur Auswahl und Planung des IT Projektportfoliomanagement Prozesses aufgestellt und in einem Entscheidungsunterstützungssystem-Prototypen integriert. Bestehende IT Projektportfoliomanagement Tools, sowie Anforderungen und unternehmerische Vorteile für IT Projektmanager wurden jeweils in Taxonomien klassifiziert, Muster erkannt und Gemeinsamkeiten und Unterschiede aufgezeigt. Zusätzlich wurden kritische Erfolgsfaktoren, Herausforderungen und Anforderungen für individuelle, digitale Studienassistenten identifiziert, analysiert und diskutiert. In mehreren Iterationen wurde basierend darauf ein Prototyp entwickelt, evaluiert, modifiziert und allgemeine Leitlinien für das Design, die Entwicklung und den Betrieb eines individuellen, digitalen Studienassistenten abgeleitet. Die Forschungsarbeiten ermöglichen IT Projektportfoliomanagement Prozesse effizienter und werteorientiert zu gestalten und subjektive Einflüsse zu minimieren sowie Hochschulen bei dem Design, der Entwicklung und dem Betrieb von individuellen, digitalen Studienassistenten zu unterstützen. Basierend auf Limitationen wird eine Forschungsagenda aufgestellt, die 13 weitere Forschungsmöglichkeiten im Themenbereich IT Projektportfoliomanagement und individuelle, digitale Studienassistenten aufzeigt und als Grundlage für weitere Forschung in diesen Themenfeldern dient.This cumulative dissertation outlines and discusses 12 scientific publications that contribute to the knowledge of Information Technology (IT) project portfolio management and individual digital study assistants in higher education. The papers developed models and frameworks that describe crucial IT project portfolio management phases and activities, enable an objective IT project evaluation, and define IT project portfolio management maturity levels. In addition, they deduced an optimization model for IT project portfolio management evaluation, selection, and scheduling decisions and implemented it in a decision support system prototype. Developed taxonomies and archetypes classify existing IT project portfolio management tools as well as requirements and corporate benefits of IT project manager positions to identify patterns, similarities, and differences. Further, critical success factors, challenges, and requirements for an individual digital study assistant were identified, analyzed, and discussed. Based on these and during several iterations, an individual digital study assistant prototype was developed, evaluated, adapted, and guidelines derived. The articles contribute knowledge on how to design more efficient and value-driven IT project portfolio management processes to minimize subjective influences. Also, they provide knowledge to support higher education institutions in the design, development, and operation of individual digital study assistants. Based on existing limitations, a further research agenda is deduced, including 13 further research directions for IT project portfolio management and individual digital study assistants in higher education institutions. They serve as a basis for further researchers in these fields of topics

Development guidelines for individual digital study assistants in higher education

Increasing student numbers, heterogeneity and individual biographies lead to a growing need for personalized support. To meet these challenges, an Individual Digital Study Assistant (IDSA) provides features to help students improve their self-regulation and organizational skills to achieve individual study goals. Based on qualitative expert interviews, a quantitative student survey, and current literature we derived requirements for an IDSA. Based on them, we designed, developed, and implemented a first IDSA prototype for higher education institutions (HEI). We continuously evaluated the prototype within different workshops and analyzed the usage data to improve it further in three enhanced prototypes. Based on this iterative process, we derived guidelines for an IDSA design and development. Accordingly, the framework, project management, content, team selection, team development, team communication, marketing, and student habits are important to consider. The guidelines advance the knowledge base of IDSA in HEI and guide and support practitioners in the design, development, and implementation of IDSA in HEI

A machine-based personality oriented team recommender for software development organizations

Hiring the right person for the right job is always a challenging task in software development landscapes. To bridge this gap, software_rms start using psychometric instruments for investigating the personality types of software practitioners. In our previous research, we have developed an MBTI-like instrument to reveal the personality types ofsoftware practitioners. This study aims to develop a personality-based team recommender mechanism to improve the e_ectiveness of software teams. The mechanism is based on predicting the possible patterns of teams using a machine-based classi_er. The classi_er is trained with em-pirical data (e.g. personality types, job roles), which was collected from52 software practitioners working on _ve different software teams. 12software practitioners were selected for the testing process who were recommended by the classi_er to work for these teams. The preliminary results suggest that a personality-based team recommender system mayprovide an effective approach as compared with ad-hoc methods of teamformation in software development organizations. Ultimately, the overallperformance of the proposed classi_er was 83.3%. These _ndings seemacceptable especially for tasks of suggestion where individuals might beable to _t in more than one team

Methodologies in Predictive Visual Analytics

abstract: Predictive analytics embraces an extensive area of techniques from statistical modeling to machine learning to data mining and is applied in business intelligence, public health, disaster management and response, and many other fields. To date, visualization has been broadly used to support tasks in the predictive analytics pipeline under the underlying assumption that a human-in-the-loop can aid the analysis by integrating domain knowledge that might not be broadly captured by the system. Primary uses of visualization in the predictive analytics pipeline have focused on data cleaning, exploratory analysis, and diagnostics. More recently, numerous visual analytics systems for feature selection, incremental learning, and various prediction tasks have been proposed to support the growing use of complex models, agent-specific optimization, and comprehensive model comparison and result exploration. Such work is being driven by advances in interactive machine learning and the desire of end-users to understand and engage with the modeling process. However, despite the numerous and promising applications of visual analytics to predictive analytics tasks, work to assess the effectiveness of predictive visual analytics is lacking. This thesis studies the current methodologies in predictive visual analytics. It first defines the scope of predictive analytics and presents a predictive visual analytics (PVA) pipeline. Following the proposed pipeline, a predictive visual analytics framework is developed to be used to explore under what circumstances a human-in-the-loop prediction process is most effective. This framework combines sentiment analysis, feature selection mechanisms, similarity comparisons and model cross-validation through a variety of interactive visualizations to support analysts in model building and prediction. To test the proposed framework, an instantiation for movie box-office prediction is developed and evaluated. Results from small-scale user studies are presented and discussed, and a generalized user study is carried out to assess the role of predictive visual analytics under a movie box-office prediction scenario.Dissertation/ThesisDoctoral Dissertation Engineering 201

Methodologies in Predictive Visual Analytics

abstract: Predictive analytics embraces an extensive area of techniques from statistical modeling to machine learning to data mining and is applied in business intelligence, public health, disaster management and response, and many other fields. To date, visualization has been broadly used to support tasks in the predictive analytics pipeline under the underlying assumption that a human-in-the-loop can aid the analysis by integrating domain knowledge that might not be broadly captured by the system. Primary uses of visualization in the predictive analytics pipeline have focused on data cleaning, exploratory analysis, and diagnostics. More recently, numerous visual analytics systems for feature selection, incremental learning, and various prediction tasks have been proposed to support the growing use of complex models, agent-specific optimization, and comprehensive model comparison and result exploration. Such work is being driven by advances in interactive machine learning and the desire of end-users to understand and engage with the modeling process. However, despite the numerous and promising applications of visual analytics to predictive analytics tasks, work to assess the effectiveness of predictive visual analytics is lacking. This thesis studies the current methodologies in predictive visual analytics. It first defines the scope of predictive analytics and presents a predictive visual analytics (PVA) pipeline. Following the proposed pipeline, a predictive visual analytics framework is developed to be used to explore under what circumstances a human-in-the-loop prediction process is most effective. This framework combines sentiment analysis, feature selection mechanisms, similarity comparisons and model cross-validation through a variety of interactive visualizations to support analysts in model building and prediction. To test the proposed framework, an instantiation for movie box-office prediction is developed and evaluated. Results from small-scale user studies are presented and discussed, and a generalized user study is carried out to assess the role of predictive visual analytics under a movie box-office prediction scenario.Dissertation/ThesisDoctoral Dissertation Engineering 201

Análise de agrupamento (Clusters Analysis) em duas etapas no ensino à distância: Uma forma de reduzir as lacunas na literatura científica no ensino à distância

Background: Dropout rates are often very high in distance education. A plethora of research has been conducted to identify the contributing factors; however, the majority of the findings are inconclusive and point to the fact that it is difficult to isolate a single explanatory factor. While frequently examined factors are personal and environmental, there is less research on the relationship between course design and retention or dropout. Method: This paper presents a study involving two-stage cluster analysis of 623 variables from 19 university courses at one open and distance education (ODE) institution. To this end, the current study grouped the courses into five types based on 22 variables. Results: The results indicate that certain sociodemographic variables become a risk factor for course dropout depending on their distribution in the standard courses. Conclusions: This result highlights the importance of instructional design in the ODE retention and dropout equation and helps explain, in part, why previous studies have not reached a consensus on which variables should be considered to explain dropout rates.Contexto: Embora as taxas de abandono escolar sejam frequentemente muito elevadas no ensino à distância, tem sido realizada muita investigação para identificar os fatores que influenciam o abandono escolar ou a persistência neste modo de aprendizagem. As conclusões destes estudos nem sempre convergem e salientam que é difícil isolar um único fator explicativo. Embora a maioria dos fatores sejam pessoais e ambientais, há menos investigação sobre a relação entre a conceção e a retenção ou desistência do curso. Método: Este estudo apresenta uma metodologia que envolve uma análise em duas fases de 623 variáveis de 19 cursos universitários de uma instituição de ensino à distância (EAD). Este estudo agrupou os cursos em cinco tipos de cursos com base em 22 variáveis. Resultados: Os resultados indicaram que certas variáveis sociodemográficas se tornam um fator de risco de desistência dos cursos, dependendo da sua distribuição nos cursos padrão. Conclusão: Esta metodologia sublinha a importância da conceção instrucional na equação de retenção e desistência da EAD e ajuda a explicar, em parte, porque é que estudos anteriores não chegaram a um consenso sobre quais as variáveis que devem ser utilizadas para explicar a desistência