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Use of CALMS to enrich learning in introductory programming courses
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In this study, components of the food-web in Macao wetlands were quantified using stable isotope ratio techniques based on carbon and nitrogen values. The δ13C and δ15N values of particulate organic matter (δ13CPOM and δ15NPOM, respectively) ranged from −30.64 ± 1.0 to −28.1 ± 0.7 ‰, and from −1.11 ± 0.8 to 3.98 ± 0.7 ‰, respectively. The δ13C values of consumer species ranged from −33.94 to −16.92 ‰, showing a wide range from lower values in a freshwater lake and inner bay to higher values in a mangrove forest. The distinct dietary habits of consumer species and the location-specific food source composition were the main factors affecting the δ13C values. The consumer 15N-isotope enrichment values suggested that there were three trophic levels; primary, secondary, and tertiary. The primary consumer trophic level was represented by freshwater herbivorous gastropods, filter-feeding bivalves, and plankton-feeding fish, with a mean δ15N value of 5.052 ‰. The secondary consumer level included four deposit-feeding fish species distributed in Fai Chi Kei Bay and deposit-feeding gastropods in the Lotus Flower Bridge flat, with a mean δ15N value of 6.794 ‰. The tertiary consumers group consisted of four crab species, one shrimp species, and four fish species in the Lotus Flower Bridge Flat, with a mean δ15N value of 13.473 ‰. Their diet mainly comprised organic debris, bottom fauna, and rotten animal tissues. This study confirms the applicability of the isotopic approach in food web studies.
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This thesis reports a mixed methods empirical research which included a university-wide survey and action research in form of a quasi-experiment in collaborative blended learning (CBL) with Macau undergraduate students. The intervention embodied the principles of social constructivism and investigated the putative benefits and challenges of CBL. The purpose of the study was to identify how to promote effective CBL in undergraduate students and to increase effective learning, motivation, autonomy, empowerment, and communication. It found that only small improvements to students’ CBL took place over time, and found that the students needed specific instruction, practice and development in how to collaborate, both with and without online learning. Despite being in a world-leading, enriched digital environment, the students were new to collaboration and online learning. Students discovered and appreciated the benefits and challenges to collaboration and CBL largely by doing it. The thesis shows that CBL does not release teachers from their instructional and pedagogical roles; rather they place teachers at the heart of effective practice and improvements. The study underpins the need for explicit training of students in CBL. It identifies several strategies and tools which can be useful to promote effective genuine CBL
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Construction projects are complex endeavours, with potential obstacles that can cause delays which can have particularly profound implications potentially impacting on company's financial health, business continuity and reputation. It is becoming increasingly recognised that delays are context-specific and multifaceted, requiring more industry-oriented perceptions. This work proposes the exploratory use of Machine Learning based on Classification and Regression Trees (CART) Decision Trees (DT) to assess the predictive analysis of these approaches, considering surveys (primary data) collected from 100 specialists with different backgrounds and experiences in the construction industry. Survey responses are discussed, followed by the CART DTs, which are used as predictor for clarifying underneath relationship among different variables in a project environment. The major issue presented is related to Project Design, with "The firm is not allowed to apply for an extension of contract period", with two possible predictors, firstly, as the main factor it is found "Mistakes, inconsistencies, and ambiguities in specification and drawing", while other aspect highlights "Poor site supervision and management by the contractor". The results indicate that the correct use of Artificial Intelligence techniques with relevant data are potential tools to support the analysis of scenarios and avoidance of project delays in Project Management.
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Despite the levels of air pollution in Macao continuing to improve over recent years, there are still days with high-pollution episodes that cause great health concerns to the local community. Therefore, it is very important to accurately forecast air quality in Macao. Machine learning methods such as random forest (RF), gradient boosting (GB), support vector regression (SVR), and multiple linear regression (MLR) were applied to predict the levels of particulate matter (PM10 and PM2.5) concentrations in Macao. The forecast models were built and trained using the meteorological and air quality data from 2013 to 2018, and the air quality data from 2019 to 2021 were used for validation. Our results show that there is no significant difference between the performance of the four methods in predicting the air quality data for 2019 (before the COVID-19 pandemic) and 2021 (the new normal period). However, RF performed significantly better than the other methods for 2020 (amid the pandemic) with a higher coefficient of determination (R2) and lower RMSE, MAE, and BIAS. The reduced performance of the statistical MLR and other ML models was presumably due to the unprecedented low levels of PM10 and PM2.5 concentrations in 2020. Therefore, this study suggests that RF is the most reliable prediction method for pollutant concentrations, especially in the event of drastic air quality changes due to unexpected circumstances, such as a lockdown caused by a widespread infectious disease.
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Validation of the Teachers’ Sense of Efficacy Scale (TSES) for use with teachers in Macao (SAR) was undertaken to determine its usefulness as a measure of teacher self-efficacy for inclusive education. This paper discusses the results found by analyzing various versions of the TSES and TSES-C in a Chinese format with 200 pre-service teachers in Macao (SAR). Psychometric analyses were undertaken to investigate the validity of the existing scales and the three and two factor solutions. The results indicated a preferred 9-item version that produced improved factor loadings and reliabilities. The use of a relatively quick and short scale to measure such a complex phenomenon as teacher self-efficacy is discussed. Issues are raised regarding generalizability of scales and the impact of culture, demographics, and edifying issues that may impact on the usefulness of such scales.
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