Towards an AEC-AI Industry Optimization Algorithmic Knowledge Mapping: An Adaptive Methodology for Macroscopic Conceptual Analysis

[EN] The Architecture, Engineering, and Construction (AEC) Industry is one of the most important productive sectors, hence also produce a high impact on the economic balances, societal stability, and global challenges in climate change. Regarding its adoption of technologies, applications and processes is also recognized by its status-quo, its slow innovation pace, and the conservative approaches. However, a new technological era - Industry 4.0 fueled by AI- is driving productive sectors in a highly pressurized global technological competition and sociopolitical landscape. In this paper, we develop an adaptive approach to mining text content in the literature research corpus related to the AEC and AI (AEC-AI) industries, in particular on its relation to technological processes and applications. We present a rst stage approach to an adaptive assessment of AI algorithms, to form an integrative AI platform in the AEC industry, the AEC-AI industry 4.0. At this stage, a macroscopic adaptive method is deployed to characterize ``Optimization,'' a key term in AEC-AI industry, using a mixed methodology incorporating machine learning and classical evaluation process. Our results show that effective use of metadata, constrained search queries, and domain knowledge allows getting a macroscopic assessment of the target concept. This allows the extraction of a high-level mapping and conceptual structure characterization of the literature corpus. The results are comparable, at this level, to classical methodologies for the literature review. In addition, our method is designed for an adaptive assessment to incorporate further stages.This work was supported by the CONICYT/FONDECYT/INICIACION under Grant 11180056 to Jose Garcia and the Spanish Ministry of Science and Innovation through the FEDER Funding under Project PID2020-117056RB-I00 to Victor Yepes.Maureira, C.; Pinto, H.; Yepes, V.; García, J. (2021). Towards an AEC-AI Industry Optimization Algorithmic Knowledge Mapping: An Adaptive Methodology for Macroscopic Conceptual Analysis. IEEE Access. 9:110842-110879. https://doi.org/10.1109/ACCESS.2021.3102215S110842110879

Defining and Assessing Critical Thinking: toward an automatic analysis of HiEd students’ written texts

L'obiettivo principale di questa tesi di dottorato è testare, attraverso due studi empirici, l'affidabilità di un metodo volto a valutare automaticamente le manifestazioni del Pensiero Critico (CT) nei testi scritti da studenti universitari. Gli studi empirici si sono basati su una review critica della letteratura volta a proporre una nuova classificazione per sistematizzare le diverse definizioni di CT e i relativi approcci teorici. La review esamina anche la relazione tra le diverse definizioni di CT e i relativi metodi di valutazione. Dai risultati emerge la necessità di concentrarsi su misure aperte per la valutazione del CT e di sviluppare strumenti automatici basati su tecniche di elaborazione del linguaggio naturale (NLP) per superare i limiti attuali delle misure aperte, come l’attendibilità e i costi di scoring. Sulla base di una rubrica sviluppata e implementata dal gruppo di ricerca del Centro di Didattica Museale – Università di Roma Tre (CDM) per la valutazione e l'analisi dei livelli di CT all'interno di risposte aperte (Poce, 2017), è stato progettato un prototipo per la misurazione automatica di alcuni indicatori di CT. Il primo studio empirico condotto su un gruppo di 66 docenti universitari mostra livelli di affidabilità soddisfacenti della rubrica di valutazione, mentre la valutazione effettuata dal prototipo non era sufficientemente attendibile. I risultati di questa sperimentazione sono stati utilizzati per capire come e in quali condizioni il modello funziona meglio. La seconda indagine empirica era volta a capire quali indicatori del linguaggio naturale sono maggiormente associati a sei sottodimensioni del CT, valutate da esperti in saggi scritti in lingua italiana. Lo studio ha utilizzato un corpus di 103 saggi pre-post di studenti universitari di laurea magistrale che hanno frequentato il corso di "Pedagogia sperimentale e valutazione scolastica". All'interno del corso, sono state proposte due attività per stimolare il CT degli studenti: la valutazione delle risorse educative aperte (OER) (obbligatoria e online) e la progettazione delle OER (facoltativa e in modalità blended). I saggi sono stati valutati sia da valutatori esperti, considerando sei sotto-dimensioni del CT, sia da un algoritmo che misura automaticamente diversi tipi di indicatori del linguaggio naturale. Abbiamo riscontrato un'affidabilità interna positiva e un accordo tra valutatori medio-alto. I livelli di CT degli studenti sono migliorati in modo significativo nel post-test. Tre indicatori del linguaggio naturale sono 5 correlati in modo significativo con il punteggio totale di CT: la lunghezza del corpus, la complessità della sintassi e la funzione di peso tf-idf (term frequency–inverse document frequency). I risultati raccolti durante questo dottorato hanno implicazioni sia teoriche che pratiche per la ricerca e la valutazione del CT. Da un punto di vista teorico, questa tesi mostra sovrapposizioni inesplorate tra diverse tradizioni, prospettive e metodi di studio del CT. Questi punti di contatto potrebbero costituire la base per un approccio interdisciplinare e la costruzione di una comprensione condivisa di CT. I metodi di valutazione automatica possono supportare l’uso di misure aperte per la valutazione del CT, specialmente nell'insegnamento online. Possono infatti facilitare i docenti e i ricercatori nell'affrontare la crescente presenza di dati linguistici prodotti all'interno di piattaforme educative (es. Learning Management Systems). A tal fine, è fondamentale sviluppare metodi automatici per la valutazione di grandi quantità di dati che sarebbe impossibile analizzare manualmente, fornendo agli insegnanti e ai valutatori un supporto per il monitoraggio e la valutazione delle competenze dimostrate online dagli studenti.The main goal of this PhD thesis is to test, through two empirical studies, the reliability of a method aimed at automatically assessing Critical Thinking (CT) manifestations in Higher Education students’ written texts. The empirical studies were based on a critical review aimed at proposing a new classification for systematising different CT definitions and their related theoretical approaches. The review also investigates the relationship between the different adopted CT definitions and CT assessment methods. The review highlights the need to focus on open-ended measures for CT assessment and to develop automatic tools based on Natural Language Processing (NLP) technique to overcome current limitations of open-ended measures, such as reliability and costs. Based on a rubric developed and implemented by the Center for Museum Studies – Roma Tre University (CDM) research group for the evaluation and analysis of CT levels within open-ended answers (Poce, 2017), a NLP prototype for the automatic measurement of CT indicators was designed. The first empirical study was carried out on a group of 66 university teachers. The study showed satisfactory reliability levels of the CT evaluation rubric, while the evaluation carried out by the prototype was not yet sufficiently reliable. The results were used to understand how and under what conditions the model works better. The second empirical investigation was aimed at understanding which NLP features are more associated with six CT sub-dimensions as assessed by human raters in essays written in the Italian language. The study used a corpus of 103 students’ pre-post essays who attended a Master's Degree module in “Experimental Education and School Assessment” to assess students' CT levels. Within the module, we proposed two activities to stimulate students' CT: Open Educational Resources (OERs) assessment (mandatory and online) and OERs design (optional and blended). The essays were assessed both by expert evaluators, considering six CT sub-dimensions, and by an algorithm that automatically calculates different kinds of NLP features. The study shows a positive internal reliability and a medium to high inter-coder agreement in expert evaluation. Students' CT levels improved significantly in the post-test. Three NLP indicators significantly correlate with CT total score: the Corpus Length, the Syntax Complexity, and an adapted measure of Term Frequency- Inverse Document Frequency. The results collected during this PhD have both theoretical and practical implications for CT research and assessment. From a theoretical perspective, this thesis shows unexplored similarities among different CT traditions, perspectives, and study methods. These similarities could be exploited to open up an interdisciplinary dialogue among experts and build up a shared understanding of CT. Automatic assessment methods can enhance the use of open-ended measures for CT assessment, especially in online teaching. Indeed, they can support teachers and researchers to deal with the growing presence of linguistic data produced within educational 4 platforms. To this end, it is pivotal to develop automatic methods for the evaluation of large amounts of data which would be impossible to analyse manually, providing teachers an

Digital Strategy Formulation: An Investigation with Design Sprints and Deep Learning

Since the invention of transistors, digital technologies have continued to have a profound impact on the global economy. Relentless performance improvements combined with convergence of digital technologies such as artificial intelligence, internet of things, and cloud computing has led to a surge in scale and importance as a source for competitive advantage. However, in 2019, only around 16% of companies managed to realize a significant improvement in business performance from digital transformation (DT). The challenges that organizations face in succeeding at DT can be traced back to strategy formulation and execution. Therefore, the aim of this research is to develop insights and tools to enhance the understanding and practice of digital strategy formulation. A comprehensive review of the literature demonstrated that DT, as an emerging body of knowledge, is lacking an in-depth and applied investigation of digital strategy formulation. The main knowledge gaps are: (1) a lack of guidance on digital strategy formulation process activities and outcomes; (2) limited consideration of the iterative nature of digital strategy formulation and validation; and (3) limited empirical investigation of digital strategy archetypes to guide the formulation process. Addressing this research gap was accomplished over three stages. First, an in-depth exploratory case study was conducted by investigating digital strategy formulation process with active participation research over six months. This investigation identified key process activities and highlighted the role of roadmapping in integrating the outcomes. Second, the findings were supplemented with literature review to design a conceptual framework for agile roadmapping to facilitate the digital strategy formulation process. This framework was then tested and calibrated over three pilot studies with companies across Europe attempting to start their DT journey. Finally, deep learning and natural language processing techniques were employed to empirically investigate the digital strategy of Fortune 500 companies from earnings call transcripts. This empirical investigation identified four digital strategy archetypes that are being employed by companies across various sectors. The findings from this research contribute to a better understanding of digital strategy formulation. It was identified that digital strategy formulation is an ongoing search process for an adequate strategic response to the DT of the economy. Specifically, incorporating agility into the formulation process is an effective way of managing the associated uncertainty of DT. Moreover, the findings demonstrated that proactively iterating between strategy formulation and validation can accelerate the realization of the emergent digital strategy. The proposed framework and the digital strategy archetypes provide a baseline for DT professionals toward a more robust digital strategy formulation.Ministry of Education - United Arab Emirate

Understanding Collaborative Sensemaking for System Design — An Investigation of Musicians\u27 Practice

There is surprisingly little written in information science and technology literature about the design of tools used to support the collaboration of creators. Understanding collaborative sensemaking through the use of language has been traditionally applied to non-work domains, but this method is also well-suited for informing hypotheses about the design collaborative systems. The presence of ubiquitous, mobile technology, and development of multi-user virtual spaces invites investigation of design which is based on naturalistic, real world, creative group behaviors, including the collaborative work of musicians. This thesis is considering the co-construction of new (musical) knowledge by small groups. Co-construction of new knowledge is critical to the definition of an information system because it emphasizes coordination and resource sharing among group members (versus individual members independently doing their own tasks and only coming together to collate their contributions as a final product). This work situates the locus of creativity on the process itself, rather than on the output (the musical result) or the individuals (members of the band). This thesis describes a way to apply quantitative observations to inform qualitative assessment of the characteristics of collaborative sensemaking in groups. Conversational data were obtained from nine face-to-face collaborative composing sessions, involving three separate bands producing 18 hours of recorded interactions. Topical characteristics of the discussion, namely objects, plans, properties and performance; as well as emergent patterns of generative, evaluative, revision, and management conversational acts within the group were seen as indicative of knowledge construction. The findings report the use of collaborative pathways: iterative cycles of generation, evaluation and revision of temporary solutions used to move the collaboration forward. In addition, bracketing of temporary solutions served to help collaborators reuse content and offload attentional resources. Ambiguity in language, evaluation criteria, goal formation, and group awareness meant that existing knowledge representations were insufficient in making sense of incoming data and necessitated reformulating those representations. Further, strategic use of affective language was found to be instrumental in bridging knowledge gaps. Based on these findings, features of a collaborative system are proposed to help in facilitating sensemaking routines at various stages of a creative task. This research contributes to the theoretical understanding of collaborative sensemaking during non-work, creative activities in order to inform the design of systems for supporting these activities. By studying an environment which forms a potential microcosm of virtual interaction between groups, it provides a framework for understanding and automating collaborative discussion content in terms of the features of dialogue

The AI4Citizen pilot: Pipelining AI-based technologies to support school-work alternation programmes

The School-Work Alternation (SWA) programme was developed (under a European Commission call) to bridge the gaps and establish a well-tuned partnership between education and the job market. This work details the development of the AI4Citizen pilot, an AI software suite designed to support the SWA programme. The AI4Citizen pilot, developed within the H2020 AI4EU project, offers AI tools to automate and enhance the current SWA process. At the same time, the AI4Citizen pilot offers novel tools to support the complex problem of allocating student teams to internship programs, promoting collaborative learning and teamwork skills acquisition. Notably, the AI4Citizen pilot corresponds to a pipeline of AI tools, integrating existing and novel technologies. Our exhaustive empirical analysis confirms that the AI4Citizen pilot can alleviate the difficulties of current processes in the SWA, and therefore it is ready for real-world deployment

State-of-the-Art Report on Systems Analysis Methods for Resolution of Conflicts in Water Resources Management

Water is an important factor in conflicts among stakeholders at the local, regional, and even international level. Water conflicts have taken many forms, but they almost always arise from the fact that the freshwater resources of the world are not partitioned to match the political borders, nor are they evenly distributed in space and time. Two or more countries share the watersheds of 261 major rivers and nearly half of the land area of the wo rld is in international river basins. Water has been used as a military and political goal. Water has been a weapon of war. Water systems have been targets during the war. A role of systems approach has been investigated in this report as an approach for resolution of conflicts over water. A review of systems approach provides some basic knowledge of tools and techniques as they apply to water management and conflict resolution. Report provides a classification and description of water conflicts by addressing issues of scale, integrated water management and the role of stakeholders. Four large-scale examples are selected to illustrate the application of systems approach to water conflicts: (a) hydropower development in Canada; (b) multipurpose use of Danube river in Europe; (c) international water conflict between USA and Canada; and (d) Aral See in Asia. Water conflict resolution process involves various sources of uncertainty. One section of the report provides some examples of systems tools that can be used to address objective and subjective uncertainties with special emphasis on the utility of the fuzzy set theory. Systems analysis is known to be driven by the development of computer technology. Last section of the report provides one view of the future and systems tools that will be used for water resources management. Role of the virtual databases, computer and communication networks is investigated in the context of water conflicts and their resolution.https://ir.lib.uwo.ca/wrrr/1005/thumbnail.jp

Predicting Paid Certification in Massive Open Online Courses

Massive open online courses (MOOCs) have been proliferating because of the free or low-cost offering of content for learners, attracting the attention of many stakeholders across the entire educational landscape. Since 2012, coined as “the Year of the MOOCs”, several platforms have gathered millions of learners in just a decade. Nevertheless, the certification rate of both free and paid courses has been low, and only about 4.5–13% and 1–3%, respectively, of the total number of enrolled learners obtain a certificate at the end of their courses. Still, most research concentrates on completion, ignoring the certification problem, and especially its financial aspects. Thus, the research described in the present thesis aimed to investigate paid certification in MOOCs, for the first time, in a comprehensive way, and as early as the first week of the course, by exploring its various levels. First, the latent correlation between learner activities and their paid certification decisions was examined by (1) statistically comparing the activities of non-paying learners with course purchasers and (2) predicting paid certification using different machine learning (ML) techniques. Our temporal (weekly) analysis showed statistical significance at various levels when comparing the activities of non-paying learners with those of the certificate purchasers across the five courses analysed. Furthermore, we used the learner’s activities (number of step accesses, attempts, correct and wrong answers, and time spent on learning steps) to build our paid certification predictor, which achieved promising balanced accuracies (BAs), ranging from 0.77 to 0.95. Having employed simple predictions based on a few clickstream variables, we then analysed more in-depth what other information can be extracted from MOOC interaction (namely discussion forums) for paid certification prediction. However, to better explore the learners’ discussion forums, we built, as an original contribution, MOOCSent, a cross- platform review-based sentiment classifier, using over 1.2 million MOOC sentiment-labelled reviews. MOOCSent addresses various limitations of the current sentiment classifiers including (1) using one single source of data (previous literature on sentiment classification in MOOCs was based on single platforms only, and hence less generalisable, with relatively low number of instances compared to our obtained dataset;) (2) lower model outputs, where most of the current models are based on 2-polar iii iv classifier (positive or negative only); (3) disregarding important sentiment indicators, such as emojis and emoticons, during text embedding; and (4) reporting average performance metrics only, preventing the evaluation of model performance at the level of class (sentiment). Finally, and with the help of MOOCSent, we used the learners’ discussion forums to predict paid certification after annotating learners’ comments and replies with the sentiment using MOOCSent. This multi-input model contains raw data (learner textual inputs), sentiment classification generated by MOOCSent, computed features (number of likes received for each textual input), and several features extracted from the texts (character counts, word counts, and part of speech (POS) tags for each textual instance). This experiment adopted various deep predictive approaches – specifically that allow multi-input architecture - to early (i.e., weekly) investigate if data obtained from MOOC learners’ interaction in discussion forums can predict learners’ purchase decisions (certification). Considering the staggeringly low rate of paid certification in MOOCs, this present thesis contributes to the knowledge and field of MOOC learner analytics with predicting paid certification, for the first time, at such a comprehensive (with data from over 200 thousand learners from 5 different discipline courses), actionable (analysing learners decision from the first week of the course) and longitudinal (with 23 runs from 2013 to 2017) scale. The present thesis contributes with (1) investigating various conventional and deep ML approaches for predicting paid certification in MOOCs using learner clickstreams (Chapter 5) and course discussion forums (Chapter 7), (2) building the largest MOOC sentiment classifier (MOOCSent) based on learners’ reviews of the courses from the leading MOOC platforms, namely Coursera, FutureLearn and Udemy, and handles emojis and emoticons using dedicated lexicons that contain over three thousand corresponding explanatory words/phrases, (3) proposing and developing, for the first time, multi-input model for predicting certification based on the data from discussion forums which synchronously processes the textual (comments and replies) and numerical (number of likes posted and received, sentiments) data from the forums, adapting the suitable classifier for each type of data as explained in detail in Chapter 7

Developing computer-based assessment as a tool to support enquiry led learning

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Master of Science (MSc) by ResearchThis research explores the possibility of developing Computer-based Assessment (CBA) as a tool to support enquiry-led learning. In this approach learners explore and unpack thoughts and ideas that help them to learn and solve problems. A critical feature of this is feedback and this research focussed on how to design and supply feedback in CBA. Two lines of research were sourced: Computer-assisted Assessment (CM) and Improving Formative Assessment (IFA). Specifically, performance data was collected, analysed and evaluated from the statistical results of 3 CSA tests (approximately 100 undergraduates per test) and from qualitative feedback, the dialogic question and answer responses of (approximately 30 learners x 100 responses) engaged on level 3 activity of the National Qualifications Framework (NQF). The outcome of the research is the development of Kilauea exemplar, a theoretical model of an enquiry led item type applied in a subject specific domain