The Influence of Mathematics Teachers' Knowledge in Technology, Pedagogy and Content (TPACK) on their Teaching Effectiveness in Saudi Public Schools

Many researchers including (Hill et al., 2008; McCray & Chen, 2012) have found that teachers' understanding of the mathematics content knowledge and their expertise in teaching methods "pedagogy" are largely responsible for how effective they are as teachers. More recent research (Lyublinskaya & Tournaki, 2012; Polly, 2011) suggests that teachers' ability to integrate technology into their teaching is also critical to their mathematics teaching effectiveness. This study investigated the validity of these assumptions for 7-12 grade mathematics teachers in Saudi Arabia and how their expertise in Technological Pedagogical And Content Knowledge (TPACK) influences their teaching effectiveness. The central question for grade 7-12 Saudi Arabian mathematics teachers is: Does expertise in technology integration, pedagogy and content relate to teaching effectiveness? The TPACK expertise of 347 secondary male mathematics teachers in Riyadh public schools was measured by self-evaluation questionnaires. Principals from 109 schools rated their teachers by using a 14 item "Teacher Effectiveness" survey. Descriptive statistics, bivariate correlations, ANOVA, Paired-Samples t-test and MANOVA were used to evaluate the relationship between the teachers' TPACK knowledge and teaching effectiveness. Results showed that teachers evaluated their TPACK at a high level. On the TPACK 1-5 Likert scale survey (5 = highly competent), the teachers rated their general mathematics content knowledge (CK) at M=3.7 (SD=.67), their general pedagogy knowledge (PK) at M=4.1 (SD =.55), their general technology knowledge (TK) at M=3.6 (SD=.70), their pedagogical knowledge within mathematics content (PCK) at M=4 (SD =.60), their technological knowledge within mathematics content (TCK) at M=3.7 (SD=.69), their technological knowledge within pedagogical knowledge (TPK) at M=3.6 (SD=.74), their technological pedagogical and content knowledge at M=3.7 (SD=.71), and their cumulative knowledge of technology, pedagogy and content at M=3.8 (SD=.52). The teachers also rated their professional preparation to integrate technology. They reported that their university courses prepared them to integrate digital technologies (M=3.51, SD=.88) better than professional development workshop and training (M=3.07, SD=1.7); t(346)= 8.17, p<.01. Principals rated the overall effectiveness of their teachers at M=3.11 (SD=.59) on the 14 item scale and their usage of technology at M=2.84 (SD=1.06). Correlations between mathematics teachers' 7 TPACK self-efficacy and the principals' rating of teacher effectiveness were not significantly different. Negative correlations were found between principals' ratings of teaching effectiveness and the teachers' evaluation of their professional preparedness in university courses (r=-.125, p<.05) and professional development training programs (r=-.129, p<.05). This discrepancy may point to differences between the way these principals and the higher education institutions value teacher preparation curriculum. Further studies may consider comparing teachers' TPACK self-efficacy to student achievement

Cross-cultural Adaption and Psychometric Properties Testing of The Arabic Anterior Knee Pain Scale

Patellofemoral pain syndrome (PFP) is a common condition affecting the musculoskeletal system and has a tendency of becoming chronic and is problematic in the affected people. It is the commonest cause of anterior knee pain. In over 2/3 of the patients affected it has been successfully treated through the use of rehabilitation protocols which are designed in pain reduction and returning the functionality to an individual. Many cases of patellofemoral pain syndrome can be avoided only if a clinician can make a pre-diagnosis. Preparation Screening Evaluation testing done by a certified athletic trainer can also help in prevention of this syndrome. The purpose of this topic is to be able to review the anatomy of the knee, the risk factors predisposing to patellofemoral pain syndrome, soft tissue, arterial system, innervation of the patellofemoral joint and strategies for rehabilitation. This will enable reviewing the anatomy of the knee, relationships between arterial collateralization, nerve supply and alignment of soft tissues in explaining the mechanisms that lead to this syndrome. By doing so, it will help in the future whereby using different treatments that will be aiming at the non-soft tissue that cause patellofemoral pain syndrome

The Use of Social Media as a Tool for Learning: Perspectives of Masters in Educational Technologies students at Bisha University, Saudi Arabia

The current research examines the attitudes towards the use of social media sites to support the learning process among Master's in Educational Technologies students at the University of Bisha in the Kingdom of Saudi Arabia, including perceptions of educational benefits as well as disadvantages and barriers. Forty two students participated in this study and completed a web-based survey. The findings revealed a largely positive attitude toward the usage of social media sites in the classroom, attributing to it many advantages, such as increasing the quality and efficiency of communication between students and teachers, greater access to information, as well as stronger social connections and ease of collaboration among classmates. However, the participants also mentioned cyberbullying, privacy issues and distractions as some difficulties associated with using these tools

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

Deep Learning for Electricity Forecasting Using Time Series Data

The complexity and nonlinearities of the modern power grid render traditional physical modeling and mathematical computation unrealistic. AI and predictive machine learning techniques allow for accurate and efficient system modeling and analysis. Electricity consumption forecasting is highly valuable in energy management and sustainability research. Furthermore, accurate energy forecasting can be used to optimize energy allocation. This thesis introduces Deep Learning models including the Convolutional Neural Network (CNN), the Recurrent neural network (RNN), and Long Short-Term memory (LSTM). The Hourly Usage of Energy (HUE) dataset for buildings in British Columbia is used as an example for our investigation, as the dataset contains data from residential customers of BC Hydro, a provincial power utility company. Due to the temporal dependency in time-series observation data, data preprocessing is required before a model can be created. The LSTM model is utilized to create a predictive model for electricity consumption as output. Approximately 63% of the data is used for training, and the remaining 37% is used for testing. Various LSTM parameters are tested and tuned for best performance. Our LSTM predictive model can facilitate power companies’ resource management decisions

Medical Image Segmentation Using Multifractal Analysis

Image segmentation plays a key role in image analysis processes. The operations performed on a segmented image tend to affect it differently than if they were performed on the original image; therefore, segmenting an image can show radically different results from the original image and successfully doing so can yield features and other important information about the image. Proper image analysis is of high importance to the medical community as accurately classifying different conditions and diseases can be facilitated with excellent patient imaging. Multifractal analysis can be leveraged for performing texture classification and image segmentation. In this paper, we propose fusion-based algorithms utilizing multifractal analysis for medical image segmentation. We use two specific multifractal masks: square and quincunx. Our techniques show new insights by using methods such as histogram decomposition in conjunction with new techniques, such as fusion. By fusing different slope images, we can extract more features thus making our proposed algorithms more robust and accurate than traditional multifractal analysis techniques. These methods are further capable of reliably segmenting medical images by implementing multifractal analysis techniques in coordination with methods such as gaussian blurring and morphological operations. The resulting image can then be easily analyzed by medical professionals for diagnosing medical conditions. The outcomes show that the proposed algorithms extract dominant features that are more encompassing and powerful than classical techniques

Identifying and characterizing lysosomal storage disease phenotypes for utilization in novel screening and monitoring assays

In the lysosomal storage disease (LSD) field there are very few studies examining large cohorts of LSD samples in order to identify suitable new pan-LSD biomarkers and identify pan-LSD disease mechanisms. This thesis investigated the possibility of using a simple fluorimetric test for lysosomal swelling, simple enzyme assays and the associated accumulation of storage material alongside the presence of unique heavy metal accumulation to identify the majority of LSDs. The results showed that lysosomal swelling is a highly sensitive phenotype and that high-throughput analysis can be achieved using the fluorescent marker lysotracker. This probe can be used to screen LSD cells as both a suitable biomarker and potentially for drug screening to develop new treatments for LSDs. This thesis was also identified that secondary alteration of lysosomal enzymes is a common feature of LSDs. Such secondary lysosomal enzyme alteration could be useful for treatment monitoring and some novel biomarkers for some and potentially all of the LSDs have emerged. I have also conducted the first electron microscopy (EM) study that compares all classes of LSDs. This technique was proven to be useful for characterisation of the lipids and other macromolecules stored both primarily and secondarily in the majority of LSDs. EM also confirmed that alteration of secondary lysosomal enzymes could be the reason behind the accumulation of materials in some LSDs. Divalent cation signalling defects have been reported in several LSDs, I therefore studied Ca2+ and trace element (TEs) ion changes across all the LSDs and discovered that lysosomal Ca2+ defects are common and that changes in Zn2+ and a few other TEs were identified in almost all or specifically altered in some of the LSDs respectively. Our results highlight the possibility of using inductively coupled plasma mass spectrometry (ICP-MS) for monitoring changes in blood TE levels during the course of clinical treatment of CLN5 patients. Finally, evidence points to the NPC1 protein function, in terms of Zn2+ efflux from lysosomes, was inhibited by common storage of sphingoid bases and is a common phenotype across the majority of LSDs that explains the occurrence of secondary lipid accumulation across most of the LSDs. Our findings provide new potential biomarkers, new mechanisms of pathogenesis and new therapeutic targets that are common to all of the LSDs validating the power of studying multiple LSDs together