Big data analytics (BDA) is an increasingly popular research area for both organisations and academia due to its usefulness in facilitating human understanding and communication. In the literature, researchers have focused on classifying big data according to data type, data security or level of difficulty, and many research papers reveal that there is a lack of information on evidence of a real-world link of big data analytics methods and its associated techniques. Thus, many organisations are still struggling to realise the actual value of big data analytic methods and its associated techniques. Therefore, this paper gives a design research account for formulating and proposing a step ahead to understand the relation between the analytical methods and its associated techniques. Furthermore, this paper is an attempt to clarify this uncertainty and identify the difference between analytics methods and techniques by giving clear definitions for each method and its associated techniques to integrate them later in a new correlation taxonomy based on the research approaches. Thus, the primary outcome of this research is to achieve for the first time a correlation taxonomy combining analytic methods used for big data and its recommended techniques that are compatible for various sectors. This investigation was done through studying various descriptive articles of big data analytics methods and its associated techniques in different industries.
Learners who enter higher education (HE) at the foundational level are susceptible to many challenges that impact their performance, engagement, and progression. Not all students who enter HE at the foundational level will progress and attain their course qualifications. In addition, many university lecturers struggle to give effective support to their students. This study focuses on feedforward teaching approaches that define ways to enhance learning by using advanced organisational strategies to offer relevant supporting concepts and meaningful verbal material. To date, there are insufficient literature reviews on feedforward approaches to facilitate students’ subsequent learning. Providing better academic support for students and a strong foundation for independent learning is the focus of this paper. Therefore, the main contributions of this paper are identifying the key feedforward features and suggesting effective feedforward approaches. This study was undertaken to rigorously implement feedforward approaches that would support groups of students in modules at the foundational entry-level. At the end of module delivery, different students’ data sets were analysed related to the progression rates, standard deviation, and mean. In addition, the student satisfaction questionnaire (module evaluation survey) and feedback survey were also considered for engagement and retention purposes. The outcomes from this exercise suggest that feedforward approaches allow students to increase their overall effort when attempting summative assessments and, thus, improve their performance, engagement, and retention.
Emotion detection (ED) and sentiment analysis (SA) play a vital role in identifying an individual’s level of interest in any given field. Humans use facial expressions, voice pitch, gestures, and words to convey their emotions. Emotion detection and sentiment analysis in English and Chinese have received much attention in the last decade. Still, poor-resource languages such as Urdu have been mostly disregarded, which is the primary focus of this research. Roman Urdu should also be investigated like other languages because social media platforms are frequently used for communication. Roman Urdu faces a significant challenge in the absence of corpus for emotion detection and sentiment analysis because linguistic resources are vital for natural language processing. In this study, we create a corpus of 1021 sentences for emotion detection and 20,251 sentences for sentiment analysis, both obtained from various areas, and annotate it with the aid of human annotators from six and three classes, respectively. In order to train large-scale unlabeled data, the bag-of-word, term frequency-inverse document frequency, and Skip-gram models are employed, and the learned word vector is then fed into the CNN-LSTM model. In addition to our proposed approach, we also use other fundamental algorithms, including a convolutional neural network, long short-term memory, artificial neural networks, and recurrent neural networks for comparison. The result indicates that the CNN-LSTM proposed method paired with Word2Vec is more effective than other approaches regarding emotion detection and evaluating sentiment analysis in Roman Urdu. Furthermore, we compare our based model with some previous work. Both emotion detection and sentiment analysis have seen significant improvements, jumping from an accuracy of 85% to 95% and from 89% to 93.3%, respectively.
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