For each unit, students spent the majority of time solving application exercise which were designed according to the ‘4S’ strategy using backwards design (Wiggins and McTighe 1998) as shown in Figure 4. In the first stage, intended outcomes of a unit should be established according to the learning outcomes for the module. In the given example, students should be able to apply knowledge on stoichiometry and material balances on multistage processes involving chemical reactions, separations and recycling. In the next stage, the team application exercise is created in such a way to assess the established learning outcomes using the 4S strategy. The given application exercise involves material balance calculations for the process composed of chemical reaction (parallel – desired and undesired reactions), separation unit and recycling system, laying the foundation for subsequent modules – unit operations, thermodynamics, reactor design and kinetics. Furthermore, it is difficult to divide tasks for individuals within the team which would cease fruitful discussions important for deep learning. Students are requested to make a specific choice by calculating various flow rates as shown in Figure 5. Depending of the application exercise, students were allowed 10 to 25 minutes to complete calculations which were followed by simultaneous answering using placards.
The changes related to the management are the most challenging in E-Learning. The very first aspect of change is learning and training which give its audience its own custom training that addresses its current job routine. The second aspect is related to change management is the deployment pace; more people will realize that they need to utilize it in their daily lives when the system is deployed and spread over the organization.
2.3 NER offset and entity classification NER offset and entity is a typical NER problem usually recognized as a sequence labeling problem. In this study, we adopted “BIO” tagging schema to represent chemical & drug mentions, where ‘B’, ‘I’ and ‘O’ represent beginning, inside and outside of a chemical & drug mentions respectively, and developed a systembased on BERT. First, character-level representation, POS tagging representation and word shape representation of each word were concatenated into the word representation of BERT, and then a CRF layer was appended to BERT for chemical & drug mentions recognition. 2.4 Concept Indexing
The data are collected in traditional learningbased on the categories academic performance, communication skill, behavior of the student, attendance. The data collected for active learning is based on the categories of foundational literacies, competence literacies and character qualities. Then the data are pre-processed, clustered to find the difference between the traditional learning and the active learning. Thus we found that Active learning is better than the traditional learning. The skill, activity, academic performance, creativity, leadership, adaptability, tolerance, intuiting power, observation capacity, are improved for the students those who are under the active learning. The sector which provide this kind of active learning also well improved in skills and their mind set are totally beyond the imaginary level. We are now at the twenty first century, with all featured items so we recommend this active learning for this new trending century. The analysis between the traditional and active learning are collected and the processes were taken under the R- tool.
Two general trends characterize the bulk of current research on NBVD. The first emphasizes SLA theory and interactionist models of learning. Data analysis typically consists of quantitative counts of the occurrence of morphological, lexical, and syntactical features in online discourse  . The second trend, described by Kern and Warschauer (2000) in the introduction to their key collection of research articles on NBVD, is informed by sociocultural and sociocognitive theories and draws on a mixture of quantitative, qualitative ethnographic, and discourse analytic methods  . At issue here is not only quantifying language development, but also understanding ho learners interpret and construct meaning online across culturally situated contexts. Although the primary research emphasis of each trend differs, the studies
English is an internationally accepted language with large number of phonemes and is being used with various accents in different parts of the world. To understand the language irrespective of the accents, the study implements a dictionary based on speech recognition using isolated characters to provide the exact meaning of the spoken word (in terms of isolated characters) with high accuracy. The entire process of speech recognition is carried out in MATLAB. The entire process to identify the pronounced character is performed in three steps. The first step performs the endpoint detection by using short-term temporal analysis. The second step includes speech feature extraction using MFCC (Mel-Frequency Cepstral Coefficients) parameters and third step is codebook generation of each of the characters using Vector Quantization LBG algorithm [Linde, Buzo and Gray] where we get the output in the form of characters on the matlab command window. These characters will be combined together to form a meaningful word. And that word will be compared with the pre-prepared database to get the audio output.
management systems and virtual learning environments merely personified the classroom environment into an online delivery format (McLoughlin and Lee 2010). While rich and interactive content existed, they relied on tasks that were prescribed and failed to align with the four key areas that were pivotal to personalized learning through digital technology: (1) allow learners to make informed educational decisions, (2) diversify and recognize different forms of skills and knowledge, (3) create diverse learning environments and (4) include learner focused forms of feedback (Green et al. 2005). As Sampson and Karadiannidis (2002) argued: “An intelligent [personalized] learning environment is capable of automatically, dynamically, and continuously adapting to the learning context, which is defined by the learner characteristics and type of educational material being exchanged.” The aforementioned seems to suggest that the failures of past systems have been a result of a lack of understanding of end-user requirements. The stipulated suggestion surrounding student disorientation and disillusionment regarding the existing ecosystem of e-learning platforms was not because they did not help, but simply because they were limited in their potential, favouring an ‘educator-first’ over a ‘student-first’ approach to design. Therefore, it is critical that end-users are engaged into the process of design to cater for student-centric learning, which implies the need for participatory design practices as a methodology to the development of computational systems in e- learning. The participatory design principles and their corresponding motivation from within the literature is briefly delineated below.
own particular learning appears glaringly evident. The estimation of this thought is in its end products. The first of these is that the more one comprehends, the all the more promptly one can learn new thoughts. Or on the other hand then again, the less one knows, the harder one can learn new things. The second is that a decent learning circumstance empowers us to experiment with thoughts more than once, making alterations, seeing what works and what does not, and utilizing this experience to refine our originations. The third is that the student must be a dynamic member, who is blending, coordinating, and attempting thoughts together. It isn't sufficient to simply enable plans to enter our psyche; they should be coordinated into existing structures and thought designs. Also, this implies for figuring out how to happen, we should be propelled to wind up occupied with the learning exercises.
• Apposition and copular feature: for each noun phrase, if it has an apposition or is followed by a copular verb, then the apposition or the subject complement is used as an attribute of that noun phrase. We also built up a dictionary where the key is the noun phrase and the value is its apposi- tion or the subject’s complement to define features. 1) i-appo-j-same-head=True, if i’s apposition and j have the same head word; 2) i-j-appo-same- head=True, if j’s apposition has the same head word as i; we define the similar head match features for the noun phrase and its complement; Also, if an i or j is a key in the defined dictionary, we get the head word of the corresponding value for that key and compare it to the head word of the other entity. • Alias feature: i-j-alias=True, if one entity is a proper noun, then we extract the first letter of each word in the other entity. ( The extraction process skips the first word if it’s a determiner and also skips the last one if it is a possessive case). If the proper noun is the same as the first-letter string, it is the alias of the other entity.
As an example, think of an online course to become a database certified engineer (DCE) as shown below in Figure 6. A learner may first look up the requirements, which are published by the database system vendor, then registers for the course, obtains a to-do list, and starts with the firstlearning object. The system knows how to assemble learning objects of a particular type, thanks to the authors’ class (and course) definition. Let us have a closer look at a “schema tuning” object, which is called after a successful processing of the class for “database administration” as well as after the “query tuning” object. The platform triggers the call of the object by using the definitions the author has made while building up the class. In fact, the author has defined different objects for “schema tuning” to be possibly used in the class. Depending on the preferences of the user and on the (time or cost) allowance, the system selects the object that fits the learner’s needs and profile best. Hence, different learners can receive different objects on the same topic if working on the same class, depending on their personal data and preferences. Let us now assume the system has chosen an object for submission to the student. Based on the metadata of the object, a Web service is called that computes the optimal presentation of the material to the learner and delivers the result of the computation. This is not a static call either, but a dynamic one for the presentation of the object. Restrictions and preferences of learner, author, and client trigger the choice of the Web service to present the material.
A speech input (an utterance) is input into the speech processing part. First, the speech features for that utterance are calculated. Next, the utterance is divided into a number of speech periods. Finally, for each speech period the speech features are extracted, and features for the utterance are compiled into a feature vector. The feature vector is then input into the emotion recognition part. In the training stage, the feature vector is used to train the neural network using back propagation. In the recognition stage, the feature vector is applied to the already trained network, and the result is a recognized emotion. These steps are explained further in the following sections.
After the invention by Carrier, air conditioners began to bloom. They first hit the industrial building such as printing plants, textile mills, pharmaceutical manufacturers, and a few hospitals. The first air-conditioned home was that of Charles Gates, son of gambler John "Bet a Million" gates, in Minneapolis in 1914. However, during the first wave their installation, Carrier's air conditioner unit were large, expensive, and dangerous due to the toxic ammonia that was used as coolant.
struct the dominant meaning tree and then use this tree to classify incoming examples from Emotion Models unit. This unit contains two types of set of words. First, set coming from Emotion Agent, which extract some features from Chatting GUI unit during the chatting between users, remove stop words, and reformulate in the way Emotion unit can deal with it. Stop words are those that occur commonly but are too general—such as “the”, “an”, “a”, “to”, etc. The al- gorithm removed the stop words from the collection. Emotion agent use Emo- tion Algorithm to assign an emotion for each set of features based on the emo- tion models coming from emotion models unit. After determining the emotion, Emotion Expression assigns a suitable expression for it and sends it to be shown in the Chatting GUI (see Figure 1).
Generally deep learning models were widely used in the field of pattern recognition and image processing. Now a days deep learning models are widely used to perform Natural Language Processing tasks. Performing sentiment analysis task using deep learning model comes under the branch of machine learning which allows good representation of learning with multiple levels of nonlinear neural networks. Sentiment analysis task was achieved by classifying the task into two levels; first one is providing the input text in the form of features and the later is the classification of the sentiment based on the provided input text features. Generally NLP [PL08, Liu12] techniques use machine learning approaches for classification of linear models such as Support Vector Machines (SVM) or Logistic Regression which takes inputs that are trained over high dimensional sparse feature vectors [Gol16].
In the first step of the test calibration, several experienced instructor were invited as courseware expert. The expert instructors analyse the learning contents, specify learning objectives for each learning contents and design suitable multiple choice items according to the learning objective at the level of remembering and understanding based on the Bloom’s Taxonomy. After that, every item will be assigned to a group of examinees and the examinees’ responses are dichotomously scored. It means that, the examinee gets one for the correct answer and zero for an incorrect answer. Then mathematical procedures are applied to the item response data by the BILOG program to obtain the value of the item parameters under 3PL model and the ability parameters of the examinees. In this stage, collaborated items are created. Then the test instructor designs a few appropriate tests consisting of 10 items at each ability level. These tests are constructed for each ability scale and will be stored in the ontological courseware database.
First year osteopathy students enrolled in the Osteopathic Science 1 and 2 (n=114) in the Bachelor of Science program at Victoria University (Melbourne, Australia) were invited to participate in the survey and focus groups. The students had access to complementary VBL material through the university learning management system (LMS). The LMS is an online system that allows students to access all the available content for their units, including resources such as lecture recordings, lecture materials and videos. The Osteopathic Science 1 and 2 units are predominately practical, where students are taught musculoskeletal examination and manual therapy skills, including articulation and soft tissue techniques for the spine and extremities. The unit is designed around students having 5-hours of face-to-face practical skill sessions each week, over a 12-week semester. Students are expected to review the supplementary material on the LMS (including the VBL content)
Table 5 shows parts of our results. There are five columns. The first column is the baseline of Last.fm’s results. The other four are the results from combination of features. Sec- tion 5.4. shows that artists with higher degree contain more correlation links to other artists, which partly reflect their influences. We show artists with the highest degree and lowest degree. From Table 5, we can see that Last.fm does not work very well for artists with low degree. Actually, we can not find similar artists information for artists with low degree on the last.fm’s website. On the contrary, our method compensates this shortage. We can see that our method performs smoothly when dealing with both high degree and low degree artists and performs 40% better on average in F-measure. Actually, the sole spectral mean dis- tance feature has already reached a pretty good precision and recall for some artists. Combined with co-occurrence features, we can see that precision and recall increased for the high degree artists whereas they did not decrease on the low degree artists. However, the co-occurrence feature alone does not perform so well. This may be due to the fact that our corpus is not large enough, so we will continue to collect data in order to improve our results in the future.
According to Silva (2009), now there is much more emphasis on what people can do with the knowledge they can access, in contrast with the fact that the possession of detailed facts and figures was once a passport to a professional job. However, many entry level roles do not get the right candidate due to the widening skill gap that exists between the learning outcomes at the university level and the expectations at the employment scenario. To illustrate, according to “The Middle East Skills Report” of a joint study conducted by Bayt.com, the Middle East’s number one job site, and YouGov, a research and consulting agency, 52 per cent of respondents in the Sultanate of Oman believe that there is a skills gap in the market. As reported in Muscat Daily (July 25, 2017), an Omani newspaper, job seekers believe that the educational system does not train students on skills which are relevant in today’s marketplace. From their perspective, there is also a “lack of awareness” of what skills are in high demand. The support of this perspective comes also from the employers. Such response from both job seekers and employers has necessitated a closer look at the present general directions in which teaching in the country is developing to nurture our students, and equip them with “lifelong learning and thinking skills necessary to acquire and process information in an ever changing world” (Cotton, 1991 cited in Karakoc, 2016, p.82). For example, while examining 21 st century higher education trends in Oman, Baporikar (2013) contends that “in order to participate in the knowledge economy a different set of human skills are required, and what truly matters is higher qualifications, intellectual independence, and flexibility” (p.141). Hence, research aimed at examining how academic programs can provide skills employers require and identifying how to better attach the term 21 st century skills to any subject for it to fit the purpose of higher education (Neisler, Clayton, Al-Barwani, Al Kharusi, & Al-Sulaimani, 2016; Tuzlukova, Al-Busaidi & Burns, 2017, etc.) is continuing to be important in Oman. One specific aspect involved in such examination is the pedagogical issue, more specifically, knowledge about the instructional strategies useful for teaching content (Grossman, 1990), enhancing students’ skills and closing the skills gap.
One of the best studied descriptive data mining methods is the association rule mining. It seeks to discover descriptive rules about relations between attributes of a set of data which exceeds a user- specified confidence threshold, i.e., each rule must cover a minimum percentage of the data. Such rules relate one or more attributes of a dataset with another attribute, producing a hypothetical if–then statement on attribute values. Mining association rules between sets of items in large databases was first proposed by Agrawal, Imielinski, and Swami (1993) and it opened up a brand new family of algorithms. The original problem came from the failure to perform the market basket analysis which attempted to find all the interesting relationships between products bought in a given context. Association rule mining was proposed for LMS in order to identify which contents students tend to access together, or which combination of tools they use .
As presented in Figure 1, the first step of our proposed work is the student takes a pre-test. Afterward, the interface agent analyzes the test result and makes a list of learning unit that should be studied by the students. This list of recommended learning unit, called learning path, is sorted ascending based on its difficulty level. The learning path is shown in Figure 5. When the students have learned a recommended learning unit, then they have to take practice test as displayed in Figure 6 and satisfy the minimum score before continuing to learn the next learning units. The students may continue to start the next learning units if their practice test score under 70% correct answer. This step then should be repeated again until the