One important subject associated with personal authentication capabilities is the analysis of handwritten signatures. Among the many known techniques, algorithms based on linguistic formalisms are also possible. However, such techniques require a number of algorithms for intelligent image analysis to be applied, allowing the development of new solutions in the field of personal authentication and building modern security systems based on the advanced recognition of such patterns. The article presents the approach based on the usage of syntactic methods for the static analysis of handwritten signatures. The graph linguistic formalisms applied, such as the IE graph and ETPL(k) grammar, are characterised by considerable descriptive strength and a polynomial membership problem of the syntactic analysis. For the purposes of representing the analysed handwritten signatures, new hierarchical (two-layer) HIE graph structures based on IE graphs have been defined. The two-layer graph description makes it possible to take into consideration both local and global features of the signature. The usage of attributed graphs enables the storage of additional semantic information describing the properties of individual signature strokes. The verification and recognition of a signature consists in analysing the affiliation of its graph description to the language describing the specimen database. Initial assessments display a precision of the method at a average level of under 75%.

The massive growth in ontology development has opened new research challenges such as ontology management, search and retrieval for the entire semantic web community. These results in many recent developments, like OntoKhoj, Swoogle, OntoSearch2, that facilitate tasks user have to perform. These semantic web portals mainly treat ontologies as plain texts and use the traditional text classification algorithms for classifying ontologies in directories and assigning predefined labels rather than using the semantic knowledge hidden within the ontologies. These approaches suffer from many types of classification problems and lack of accuracy, especially in the case of overlapping ontologies that share common vocabularies. In this paper, we define an ontology classification problem and categorize it into many sub-problems. We present a new ontological methodology for the classification of web ontologies, which has been guided by the requirements of the emerging Semantic Web applications and by the lessons learnt from previous systems. The proposed framework, OntClassifire, is tested on 34 ontologies with a certain degree of overlapping domain, and effectiveness of the ontological mechanism is verified. It benefits the construction, maintenance or expansion of ontology directories on the semantic web that help to focus on the crawling and improving the quality of search for the software agents and people. We conclude that the use of a context specific knowledge hidden in the structure of ontologies gives more accurate results for the ontology classification.

L'actualité aidant, une personne sur twitter a exhumé un vieux billet que j'avais écrit en Mai 2011. A l'époque, je parlais du Parti Socialiste... sans fausse modestie, j'avais vu plutôt juste... Aujourd'hui, on peut déjà parler d'un autre "parti" mais...

Depuis quelques années, la théorie du grand remplacement, popularisée par Michel Houellbecq dans Soumissions ou par un Eric Zemmour, a fait son chemin dans les arcanes les moins visibles du Net. Pourtant, le danger n’est pas là, loin s’en faut, il est...

Tensions à l'Université Lyon-3 : la classe politique condamne les attaques contre Yaël Braun-Pivet

1. Vendée Globe. Coup dur pour Clarisse Crémer : elle a perdu son grand gennaker  Voiles et Voiliers
2. Vendée Globe : «J’ai un peu honte», la tuile pour Clarisse Crémer qui perd sa voile principale dans l’océan  Le Figaro
3. Vendée Globe : Crémer a perdu sa grande voile  Toute l'actualité sportive sur Orange
4. VIDÉO. Vendée Globe : « Les gestes techniques de Clacla », Crémer se console après son avarie  Ouest-France
5. Vendée Globe 2024 : Clarisse Crémer perd sa plus grande voile au large du Portugal  Le Journal des Sables

Dans la douzième étape, entre Bourg-Saint-Maurice et l’Alpe d’Huez (175,5 km), nouvelle victoire du maillot jaune Geraint Thomas (Sky), qui se comporte de plus en plus en leader. Il s’agissait de la plus belle étape de montagne, avec trois grands cols hors catégorie.

Alpe d’Huez (Isère), envoyé spécial.
Tout de nerfs et de cernes, le peloton s’étirait déjà en lambeaux et nous voyions clairement à travers depuis un moment. Devant, derrière, un peu partout, un dialogue spumescent et ...

(Belga) Le conseil communal de la Ville de Charleroi a adopté lundi une motion faisant de Charleroi "une ville antifasciste" et consacrant l'existence d'une "coalition antifasciste" composée des partis politiques carolos, des syndicats, d'associations et de membres de la société civile.

Cette "coalition antifasciste" est le fruit de discussions entamées dans un contexte de montée générale des idées d'extrême droite et à la suite des incidents qui sont survenus le 25 janvier 2020 à Charleroi à l'occasion d'une mobilisation d'un front antifasciste contre la tenue dans la métropole d'une réunion d'un nouveau parti d'extrême droite. Ce jour-là, selon les manifestants antifascistes, la police avait fait usage contre eux de sprays, d'autopompes et de coups de matraques même pour les disperser. Ce qui avait provoqué un certain émoi, y compris au sein de la classe politique carolo. La motion donne à la coalition antifasciste quelques objectifs généraux, comme celui "d'empêcher par tous les moyens légaux la diffusion de propos incitant à la haine, au racisme, à l'antisémitisme, au sexisme, à la discrimination relative à l'orientation sexuelle, ouvertement fasciste et xénophobe, sur le territoire de Charleroi" ou celui de relayer l'information "lorsqu'elle concerne un événement susceptible d'inciter à la haine, au racisme, à l'antisémitisme, au sexisme, ouvertement fasciste et xénophobe". (Belga)

Voilￜ un "petit oubli" bien embￜtant. Selon Mediapart, "le garde des Sceaux, Eric Dupont-Moretti, a oubliￜ de dￜclarer au fisc et ￜ la Haute Autoritￜ pour la transparence de la vie publique...

Image forgery detection techniques refer to the process of detecting manipulated or altered images, which can be used for various purposes, including malicious intent or misinformation. Image forgery detection is a crucial task in digital image forensics, where researchers have developed various techniques to detect image forgery. These techniques can be broadly categorised into active, passive, machine learning-based and hybrid. Active approaches involve embedding digital watermarks or signatures into the image during the creation process, which can later be used to detect any tampering. On the other hand, passive approaches rely on analysing the statistical properties of the image to detect any inconsistencies or irregularities that may indicate forgery. In this paper for the detection of scaling and cropping attack a deep learning method has been proposed using ResNet. The proposed method (Res-Net-Adam-Adam) is able to achieve highest amount of accuracy of 99.14% (0.9914) while detecting fake and real images.

In order to further improve the teaching quality of multimedia teaching in school daily teaching, a classroom facial expression emotion recognition model is proposed based on convolutional neural network. VGGNet and CliqueNet are used as the basic expression emotion recognition methods, and the two recognition models are fused while the attention module CBAM is added. Simulation results show that the designed classroom face expression emotion recognition model based on V-CNet has high recognition accuracy, and the recognition accuracy on the test set reaches 93.11%, which can be applied to actual teaching scenarios and improve the quality of classroom teaching.

Technology skills are assumed to be a necessity for college and career success, but technology is constantly evolving. Thus, development of students’ technology skills is an on-going and persistent issue. Standards from the Partnership for 21st Century Skills and the International Society for Technology in Education encourage educators to teach skills that help students adapt to changing working environments. These skills resemble the National Association for Gifted Children’s program and teacher preparation standards. Descriptive research about what is already occurring in classrooms has been done, but the information is frequently limited to a list of activities. A qualitative multi-case phenomenological study of six Alabama teachers of the gifted examined how they use and shape technology experiences with students, and promote student learning of 21st century skills. The teachers were chosen for the case study due to their reputation as teachers skilled in using technology with students. Lesson plans, interviews, and observations were used to discover themes between the teachers. Findings from the research indicate that educational technology use with students is shaped by factors such as teacher attitudes and expertise, available equipment and support, pedagogical decisions related to working with technology, and the particular student group participating in the technology use.

Increasingly, numerous universities have identified benefits of flipped learning environments and have been encouraging instructors to adapt such methodologies in their respective classrooms, at a time when departments are facing significant budget constraints. This article proposes an instructional design framework utilized to strategically enhance traditional flipped methodologies in a first-year engineering course, by using low-cost technology aids and proven pedagogical techniques to enhance student learning. Implemented in a first-year engineering course, this modified flipped model demonstrated an improved student awareness of essential engineering concepts and improved academic performance through collaborative and active learning activities, including flipped learning methodologies, without the need for expensive, formal active learning spaces. These findings have been validated through two studies and have shown similar results confirming that student learning is improved by the implementation of multi-pedagogical strategies in-formed by the use of an instructional design in a traditional classroom setting.

Aim/Purpose: The present study investigated and compared students’ attention in terms of time-on-task and number of distractors between using a touchscreen and a pen tablet in mathematical problem-solving activities with virtual manipulatives. Background: Although there is an increasing use of these input devices in educational practice, little research has focused on assessing student attention while using touchscreens or pen tablets in a mathematics classroom. Methodology: A qualitative exploration was conducted in a public elementary school in New Taipei, Taiwan. Six fifth-grade students participated in the activities. Video recordings of the activities and the students’ actions were analyzed. Findings: The results showed that students in the activity using touchscreens maintained greater attention and, thus, had more time-on-task and fewer distractors than those in the activity using pen tablets. Recommendations for Practitioners: School teachers could employ touchscreens in mathematics classrooms to support activities that focus on students’ manipulations in relation to the attention paid to the learning content. Recommendation for Researchers: The findings enhance our understanding of the input devices used in educational practice and provide a basis for further research. Impact on Society: The findings may also shed light on the human-technology interaction process involved in using pen and touch technology conditions. Future Research: Activities similar to those reported here should be conducted using more participants. In addition, it is important to understand how students with different levels of mathematics achievement use the devices in the activities.

Aim/Purpose : To examine the use of a flipped classroom in the English Language subject in secondary classrooms in Hong Kong. Background: The research questions addressed were: (1) What are teachers’ perceptions towards the flipped classroom pedagogy? (2) How can teachers transfer their flipped classroom experiences to teaching other classes/subjects? (3) What are students’ perceptions towards the flipped classroom pedagogy? (4) How can students transfer their flipped classroom experiences to studying other subjects? (5) Will students have significant gain in the knowledge of the lesson topic trialled in this study? Methodology: A total of 57 students from two Secondary 2 classes in a Band 3 secondary school together with two teachers teaching these two classes were involved in this study. Both quantitative and quantitative data analyses were conducted. Contribution: Regarding whether the flipped classroom pedagogy can help students gain significantly in their knowledge of a lesson topic, only one class of students gained statistically significantly in the subject knowledge but not for another class. Findings: Students in general were positive about the flipped classroom. On the other hand, although the teachers considered that the flipped classroom pedagogy was creative, they thought it may only be useful for teaching English grammar. Recommendations for Practitioners: Teachers thought that flipping a classroom may only be useful for more motivated students, and the extra workload of finding or making suitable pre-lesson online videos is the main concern for teachers. Recommendations for Researchers: Both quantitative and qualitative analyses should be conducted to investigate the effectiveness of a flipped classroom on students’ language learning. Impact on Society : Teachers and students can transfer their flipped classroom experiences in English Language to teaching and studying other subjects. Future Research: More classes should be involved and a longer period of time should be spent on trial teaching in which a flipped classroom can be implemented in different lesson topics, not only teaching grammar. Teachers also need to determine if students can use the target language item in a task.

Aim/Purpose: To identify positive and negative aspects for learning of interactive tablet technology learning activities that promote student engagement and learning. Background: Engaging students in mathematics classes is an on-going challenge for teachers. In 2008 we were offered the opportunity to run interactive activities with a class set of tablet PCs that had just been released on to the market. Since then, we have run these interactive activities continuously with mathematics classes for computing students, albeit with two changes in hardware. Methodology: In the interactive activities, students submit full worked solutions to various problem types (classified as table, text, open or multi-choice) which can then be displayed to the class anonymously, discussed and annotated by the teacher. We surveyed student and staff perceptions and monitored academic performance. Contribution: We have over 10 years of results, observations, and experience from 2008, when tablet technologies were new and expensive, to the current time, when modern tablets with styli are now affordable. Findings: There was a significant increase in higher grades although pass rates did not increase significantly. Over the ten year period of the study, perceptions of students and staff about how this technology impacted on student learning were consistently positive. The majority of students found all problem types useful for learning even those they rated “too hard” or “too easy”. Benefits included increased feedback, peer learning and engagement. Recommendations for Practitioners: We recommend using tablet learning activities to engage students and teachers and to contribute to learning. Impact on Society: This study shows how using tablet technologies for interactive classroom activities can enable and enhance known pedagogies of feedback, peer instruction, and student engagement for mathematics classes. Future Research: We recommend extending this study to include larger classes, and other technical subjects that use symbols and diagrams. In addition, we suggest considering control groups.

Aim/Purpose: This paper presents a data mining approach for analyzing responses to advanced declarative programming questions. The goal of this research is to find a model that can explain the results obtained by students when they perform exams with Constructed Response questions and with equivalent Multiple-Choice Questions. Background: The assessment of acquired knowledge is a fundamental role in the teaching-learning process. It helps to identify the factors that can contribute to the teacher in the developing of pedagogical methods and evaluation tools and it also contributes to the self-regulation process of learning. However, better format of questions to assess declarative programming knowledge is still a subject of ongoing debate. While some research advocates the use of constructed responses, others emphasize the potential of multiple-choice questions. Methodology: A sensitivity analysis was applied to extract useful knowledge from the relevance of the characteristics (i.e., the input variables) used for the data mining process to compute the score. Contribution: Such knowledge helps the teachers to decide which format they must consider with respect to the objectives and expected students results. Findings: The results shown a set of factors that influence the discrepancy between answers in both formats. Recommendations for Practitioners: Teachers can make an informed decision about whether to choose multiple-choice questions or constructed-response taking into account the results of this study. Recommendation for Researchers: In this study a block of exams with CR questions is verified to complement the area of learning, returning greater performance in the evaluation of students and improving the teaching-learning process. Impact on Society: The results of this research confirm the findings of several other researchers that the use of ICT and the application of MCQ is an added value in the evaluation process. In most cases the student is more likely to succeed with MCQ, however if the teacher prefers to evaluate with CR other research approaches are needed. Future Research: Future research must include other question formats.

Aim/Purpose: In this study, the researchers investigated whether the out-of-class learning approach could help the students to attain any valuable learning outcomes for cybersecurity learning and could enhance the perceived value of cybersecurity education among the students. Background: Cybersecurity learning poses challenges for its students to learn a complicated subject matter and the students may be intimidated by the challenging courses in cybersecurity programs. Therefore, it is essential for the faculty members to devise some mechanisms to promote cybersecurity learning to increase its student retention. The mechanism suggested by this study was the out-of-class learning approach. Methodology: The researchers in this study employed a content analysis and adopted a semiotic method to analyze qualitative data. The researchers also conducted crosstabulation analyses using chi-square tests to detect the significant differences in the emerging learning outcomes from the two different out-of-class learning activities investigated in this study. Contribution: This study addressed the difficulty of cybersecurity education and proposed a viable mechanism to promote the student learning in such a complicated subject matter. Findings: For cybersecurity education, the out-of-class learning approach is a viable pedagogical mechanism that could lead the students to several learning outcomes, including connecting them to the real-life scenarios related to the cybersecurity profession, guiding them to their career choices and development, stimulating their intellectual growth, creating their justification of learning, and raising their cybersecurity awareness. Recommendations for Practitioners: The instructors of any cybersecurity programs should incorporate some out-of-class learning activities into the courses in their programs, especially the introductory-level courses. Additionally, it is important to coordinate the out-of-class learning activities with the in-class lessons to enable the students to justify what they have learned in their classrooms and motivate them to learn more. Recommendation for Researchers: Researchers could look beyond in-class learning and laboratory learning to investigate the impacts of out-of-class learning activities on cybersecurity education to help the students to attain better learning outcomes. Impact on Society: By promoting cybersecurity education, universities and colleges could attain a higher retention rate of the students in their cybersecurity programs. The higher retention rate of the students in cybersecurity programs would help to ease the critical shortage of cybersecurity talent. Future Research: Future research could explore the impacts of other out-of-class learning activities on cybersecurity learning; for example: job shadowing, attending cybersecurity conferences, internship, developing cybersecurity systems or tools for actual customers, working on cybersecurity research with faculty members. Additionally, future studies could investigate the effects of the out-of-class learning approach on promoting other academic programs that are characterized by intensely complex and technical nature, similar to cybersecurity programs.

Aim/Purpose: Students tend to learn best when an array of learning styles is used by instructors. The purpose of this paper is to add, to introduce, and to apply the concepts of kinesthetic learning and learning structures to university and STEM education. Background: The study applies the concept of kinesthetic learning and a learning structure called Think-Pair-Share to an experiential exercise about Moore’s Law in an introductory MIS classroom. The paper details the exercise and each of its components. Methodology: Students in two classes were asked to complete a short survey about their conceptual understanding of the course material before and after the experiential exercise. Contribution: The paper details the benefits of kinesthetic learning and learning structures and discusses how to apply these concepts through an experiential exercise used in an introductory MIS course. Findings: Results indicate that the kinesthetic learning activity had a positive impact on student learning outcomes. Recommendations for Practitioners: University educators can use this example to structure several other learning activities that apply kinesthetic learning principles. Recommendation for Researchers: Researchers can use this paper to study more about how to incorporate kinesthetic learning into education, and about teaching technology concepts to undergraduate students through kinesthetic learning. Impact on Society: The results of this study may be extremely beneficial for the university and STEM community and overall academic business community. Future Research: Researchers should consider longitudinal studies and other ways to incorporate kinesthetic learning activities into education.

Aim/Purpose: The complexity of today’s organizational databases highlights the importance of hard technical skills as well as soft skills including teamwork, communication, and problem-solving. Therefore, when teaching students about databases it follows that using a team approach would be useful. Background: Team-based learning (TBL) has been developed and tested as an instructional strategy that leverages learning in small groups in order to achieve increased overall effectiveness. This research studies the impact of utilizing team-based learning strategies in an undergraduate Database Management course in order to determine if the methodology is effective for student learning related to database technology concepts in addition to student preparation for working in database teams. Methodology: In this study, a team-based learning strategy is implemented in an undergraduate Database Management course over the course of two semesters. Students were assessed both individually and in teams in order to see if students were able to effectively learn and apply course concepts on their own and in collaboration with their team. Quantitative and qualitative data was collected and analyzed in order to determine if the team approach improved learning effectiveness and allowed for soft skills development. The results from this study are compared to previous semesters when team-based learning was not adopted. Additionally, student perceptions and feedback are captured. Contribution: This research contributes to the literature on database education and team-based learning and presents a team-based learning process for faculty looking to adopt this methodology in their database courses. This research contributes by showing how the collaborative assessment aspect of team-based learning can provide a solution for the conceptual and collaborative needs of database education. Findings: Findings related to student learning and perceptions are presented illustrating that team-based learning can lead to improvements in performance and provides a solution for the conceptual and collaborative needs of database education. Specifically, the findings do show that team scores were significantly higher than individual scores when completing class assessments. Student perceptions of both their team members and the team-based learning process were overall positive with a notable difference related to the perception of team preparedness based on gender. Recommendations for Practitioners: Educational implications highlight the challenges of team-based learning for assessment (e.g., gender differences in perceptions of team preparedness), as well as the benefits (e.g., development of soft skills including teamwork and communication). Recommendation for Researchers: This study provides research implications supporting the study of team assessment techniques for learning and engagement in the context of database education. Impact on Society: Faculty looking to develop student skills in relation to database concepts and application as well as in relation to teamwork and communication may find value in this approach, ultimately benefiting students, employers, and society. Future Research: Future research may examine the methodology from this study in different contexts as well as explore different strategies for group assignments, room layout, and the impact of an online environment.

Aiming to improve the accuracy, recall, and F1 value of data classification, this paper proposes an applied university innovation and entrepreneurship data classification method based on the nearest neighbour criterion. Firstly, the decision tree algorithm is used to mine innovation and entrepreneurship data from applied universities. Then, dynamic weight is introduced to improve the similarity calculation method based on edit distance, and the improved method is used to realise data de-duplication to avoid data over fitting. Finally, the nearest neighbour criterion method is used to classify applied university innovation and entrepreneurship data, and cosine similarity is used to calculate the similarity between the samples to be classified and each sample in the training data, achieving data classification. The experimental results demonstrate that the proposed method achieves a maximum accuracy of 96.5% and an average F1 score of 0.91. These findings indicate a high level of accuracy, recall, and F1 value for data classification using the proposed method.

In order to overcome the problems of low evaluation accuracy and long evaluation time in traditional teaching quality evaluation methods, a method of English distance information teaching quality evaluation based on decision tree classification algorithm is proposed. Firstly, construct teaching quality evaluation indicators under different roles. Secondly, the information gain theory in decision tree classification algorithm is used to divide the attributes of teaching resources. Finally, the rough set theory is used to calculate the index weight and establish the risk evaluation index factor set. The result of teaching quality evaluation is obtained through fuzzy comprehensive evaluation method. The experimental results show that the accuracy rate of the teaching quality evaluation of this method can reach 99.2%, the recall rate of the English information teaching quality evaluation is 99%, and the time used for the English distance information teaching quality evaluation of this method is only 8.9 seconds.

In order to improve the standardisation of mutual information index, accuracy rate and recall rate of student classroom behaviour recognition method, this paper proposes a student's classroom behaviour recognition method based on abstract hidden Markov model (HMM). After cleaning the students' classroom behaviour data, improve the data quality through interpolation and standardisation, and then divide the types of students' classroom behaviour. Then, in support vector machine, abstract HMM is used to calculate the output probability density of support vector machine. Finally, according to the characteristic interval of classroom behaviour, we can judge the category of behaviour characteristics. The experiment shows that normalised mutual information (NMI) index of this method is closer to one, and the maximum AUC-PR index can reach 0.82, which shows that this method can identify students' classroom behaviour more effectively and reliably.

Multi-criteria inventory classification (MCIC) is popularly used to aid managers in categorising the inventory. Researchers have used numerous mathematical models and approaches, but few resorted to unsupervised machine-learning techniques to address MCIC. This study uses the expectation-maximisation (EM) algorithm to estimate the parameters of the Gaussian mixture model (GMM), a popular unsupervised machine learning algorithm, for ABC inventory classification. The EM-GMM algorithm is sensitive to initialisation, which in turn affects the results. To address this issue, two different initialisation procedures have been proposed for the EM-GMM algorithm. Inventory classification outcomes from 14 existing MCIC models have been given as inputs to study the significance of the two proposed initialisation procedures of the EM-GMM algorithm. The effectiveness of these initialisation procedures corresponding to various inputs has been analysed toward inventory management performance measures, i.e., fill rate, total relevant cost, and inventory turnover ratio.

This longitudinal, quasi-experimental study investigated students’ cognitive personality type using the Myers-Briggs personality Type Indicator (MBTI) in Internet-based Online and Face-to-Face (F2F) modalities. A total of 1154 students enrolled in 28 Online and 32 F2F sections taught concurrently over a period of fourteen years. The study measured whether the sample is similar to the national average percentage frequency of all 16 different personality types; whether specific personality type students preferred a specific modality of instructions and if this preference changed over time; whether learning occurred in both class modalities; and whether specific personality type students learned more from a specific modality. Data was analyzed using regression, t-test, frequency, and Chi-Squared. The study concluded that data used in the study was similar to the national statistics; that no major differences in preference occurred over time; and that learning did occur in all modalities, with more statistically significant learning found in the Online modality versus F2F for Sensing, Thinking, and Perceiving types. Finally, Sensing and Thinking (ST) and Sensing and Perceiving (SP) group types learned significantly more in Online modality versus F2F.

The impact that mobile web-enabled devices have had on the lives and behavior of university students has been immense. Yet, many of the models used in the classrooms have remained unchanged. Although a traditional research approach of examining the literature, developing a methodology, and so on is followed, this paper’s main aim is to inform practitioners on observations and examples from courses which insist on and encourage mobiles in the classroom. The paper asked three research questions regarding the use, impact, and unintended consequences of mobile web-enabled devices in the classroom. Data was collected from observing and interacting with post graduate students and staff in two universities across two continents: Africa and Europe. The paper then focuses on observations and examples on the use, impact, and unintended consequences of mobile web-enabled devices in two classrooms. The findings are that all students used mobile web-enabled devices for a variety of reasons. The use of mobile devices did not negatively impact the class, rather students appeared to be more engaged and comfortable knowing they were allowed to openly access their mobile devices. The unintended consequences included the use of mobiles to translate text into home languages.

The study involved student teachers enrolled in early childhood teaching at a teacher training institute in Hong Kong Special Administrative Region. Seventy-four students participated in flipped classroom activities during their first semester of study. Students were told to learn from online videos related to using image editing software in their own time and pace prior to the next class. When they met in class, they were asked to apply their recently acquired editing knowledge to edit an image of their own choice related to the theme of their group project. At the end of the activity, students were asked to complete an online questionnaire. It was found that students had rated all five questions relating to generic skills highly, with self-study skills rated the highest. They particularly enjoyed the flexibility of learning on their own time and pace as a benefit of the flipped classroom. Data collected from students’ project pages show they had used average of 3.22 editing features for the theme images for their project. Most groups had inserted text followed by using the filter function. It is possible that these two functions are more noticeable than other editing functions. In conclusion, students were able to apply their self-learnt knowledge in a real-life situation and they had also developed their generic skills via the flipped classroom pedagogy.

The authors present the results of a study conducted at a comprehensive, urban, coeducational, land-grant university. A quasi-experimental design was chosen for this study to compare student performance in two different classroom environments, traditional versus flipped. The study spanned 3 years, beginning fall 2012 through spring 2015. The participants included 433 declared business majors who self-enrolled in several sections of the Management Information Systems course during the study. The results of the current study mirrored those of previous works as the instructional method impacted students’ final grade. Thus, reporting that the flipped classroom approach offers flexibility with no loss of performance when compared to traditional lecture-based environments.

Aim/Purpose: Teachers are being asked to integrate mobile technologies into their content creation and distribution tasks. This research aims to provide an understanding of teachers taking on this process and whether the use of technology has influenced their content creation and distribution in the classroom. Background: Many claim that the use of technology for content creation and distribution can only enhance and improve the educational experience. However, for teachers it is not simply the integration of technology that is of prime concern. As teachers are ultimately responsible for the success of technology integration, it is essential to understand teachers’ viewpoints and lived technology experiences. Methodology: The Task-Technology Fit (TTF) model was used to guide interpretive case study research. Six teachers were purposively sampled and interviewed from a private school where a digital strategy is already in place. Data was then analysed using directed content analysis in relation to TTF. Contribution: This paper provides an understanding of teachers’ mobile technology choices in relation to content creation and distribution tasks. Findings: Findings indicate that teachers fit technology into their tasks if they perceive the technology has a high level of benefit to the teaching task. In addition, the age of learners and the subject being taught are major influencers. Recommendations for Practitioners: Provides a more nuanced and in-depth understanding of teachers’ technology choices, which is necessary for the technology augmented educational experience of the future. Recommendations for Researchers: Provides an unbiased and theoretically guided view of mobile technology use with content creation and distribution tasks. Impact on Society: Teachers do not appear to use technology as a de facto standard, but specifically select technology which will save them time, reduce costs, and improve the educational experiences of their learners. Future Research: A mixed-method approach, including several diverse schools as well as learners would enrich the findings. Furthermore, consideration of hardware limitations and lack of software features are needed.

Aim/Purpose: The aim of this study was to develop a prototype of an information-generating computer tool designed to automatically map the dental restorations in a panoramic radiograph. Background: A panoramic radiograph is an external dental radiograph of the oro-maxillofacial region, obtained with minimal discomfort and significantly lower radiation dose compared to full mouth intra-oral radiographs or cone-beam computed tomography (CBCT) imaging. Currently, however, a radiologic informative report is not regularly designed for a panoramic radiograph, and the referring doctor needs to interpret the panoramic radiograph manually, according to his own judgment. Methodology: An algorithm, based on techniques of computer vision and machine learning, was developed to automatically detect and classify dental restorations in a panoramic radiograph, such as fillings, crowns, root canal treatments and implants. An experienced dentist evaluated 63 panoramic anonymized images and marked on them, manually, 316 various restorations. The images were automatically cropped to obtain a region of interest (ROI) containing only the upper and lower alveolar ridges. The algorithm automatically segmented the restorations using a local adaptive threshold. In order to improve detection of the dental restorations, morphological operations such as opening, closing and hole-filling were employed. Since each restoration is characterized by a unique shape and unique gray level distribution, 20 numerical features describing the contour and the texture were extracted in order to classify the restorations. Twenty-two different machine learning models were evaluated, using a cross-validation approach, to automatically classify the dental restorations into 9 categories. Contribution: The computer tool will provide automatic detection and classification of dental restorations, as an initial step toward automatic detection of oral pathologies in a panoramic radiograph. The use of this algorithm will aid in generating a radiologic report which includes all the information required to improve patient management and treatment outcome. Findings: The automatic cropping of the ROI in the panoramic radiographs, in order to include only the alveolar ridges, was successful in 97% of the cases. The developed algorithm for detection and classification of the dental restorations correctly detected 95% of the restorations. ‘Weighted k-NN’ was the machine-learning model that yielded the best classification rate of the dental restorations - 92%. Impact on Society: Information that will be extracted automatically from the panoramic image will provide a reliable, reproducible radiographic report, currently unavailable, which will assist the clinician as well as improve patients’ reliance on the diagnosis. Future Research: The algorithm for automatic detection and classification of dental restorations in panoramic imaging must be trained on a larger dataset to improve the results. This algorithm will then be used as a preliminary stage for automatically detecting incidental oral pathologies exhibited in the panoramic images.

Aim/Purpose: This paper presents an exploratory case study into using 360° videos to present small segments of lecture content for IT students in an Australian University. The aim of this study was to understand; what is the impact of incorporating 360° videos into class content for students and teaching staff? In this study the 360° videos are described as “learning atoms”. Learning atoms are short duration videos (1 to 5 minutes) captured in 360°. Background: Within this paper we conducted experiments in the classroom using 360° videos to determine if they have an impact on student's feeling of presence with class content. Additionally, to follow up, how does the inclusion of 360° impact on the teaching experience. Methodology: The methodology used in this study focused on both quantitative and qualita-tive aspects. Data was captured at the same time during the teaching period to address the research questions. In order to gauge the feeling of presence within the classroom a short survey was administered to students in the undergraduate IT class at the start (pre) and end (post) of the semester using the same questions to measure any change. Contribution: The main contributions from this study were that we demonstrated there is a potential for providing an alternative ‘immersive’ content presentation for students. This alternative content took the form of 360° learning atoms, whereas further showed our nuance process for creating and publishing of these atoms. Findings: The results show that for students, learning atoms can help improve the sense of presence, particularly for remote students, however the interactive experience can take student’s attention away from the lecturer. The results present potential for providing an alternative ‘immersive’ content presentation for students, however problems for uptake are present for both students and teachers, such as image capture quality and file size Impact on Society: We foresee this approach as being a new approach to teaching students in higher education within online spaces to increase engagement and move towards having a richer virtual experience no matter the location. Future Research: Future research will be conducted to resolve whether presence and engagement is supported by the inclusion of 360° videos in the classroom.

Aim/Purpose: The flipped classroom approach is one of the most popular active learning approaches. This paper explores the effectiveness of a new pedagogy, known as FOCUSED, for postgraduate students. Background: The flipped classroom approach is a trendy blended learning pedagogy which capitalizes on the flexibility of online learning and the stimulating nature of face-to-face discussion. This article describes a pilot study involving post-graduate students who experienced the flipped classroom approach in one of their courses. Methodology: In additional to online activities, students adopted a newly learned approach to solve a related problem that was given by another group of students during classes. Quantitative data were collected from pre- and post-tests for both self-learned online materials and group discussion during classes so that the effectiveness of the flipped classroom pedagogy could be examined from the perspective of a holistic learning experience. Findings: It was found that the average scores for the post-test for the self-learned online video were much higher than for pre-test, even though the post-tests for both online and face-to-face learning were higher than the respective pre-tests. The qualitative data collected at the end of the flipped classroom activities further confirmed the value of the flipped classroom approach. Even though students could self-learn, more students valued peer interactions in the classroom more than the flexibility of online learning.

Aim/Purpose: This brief paper will provide preliminary insight into an institutions effort to help students understand the application of the scientific method as it applies to the business discipline through the creation of a dedicated, required course added to the curriculum of a mid-Atlantic minority-serving institution. In or-der to determine whether the under-consideration course satisfies designated student learning outcomes, an assessment regime was initiated that included examination of rubric data as well as the administration of a student perception survey. This paper summarizes the results of the early examination of the efficacy of the course under consideration. Background: A small, minority-serving, university located in the United States conducted an assessment and determined that students entering a department of business following completion of their general education science requirements had difficulties transferring their understanding of the scientific method to the business discipline. Accordingly, the department decided to create a unique course offered to sophomore standing students titled Principles of Scientific Methods in Business. The course was created by a group of faculty with input from a twenty person department. Methodology: Rubrics used to assess a course term project were collected and analyzed in Microsoft Excel to measure student satisfaction of learning goals and a student satisfaction survey was developed and administered to students enrolled in the course under consideration to measure perceived course value. Contribution: While the scientific method applies across the business and information disciplines, students often struggle to envision this application. This paper explores the implications of a course specifically purposed to engender the development and usage of logical and scientific reasoning skills in the business discipline by students in the lower level of an bachelors degree program. The information conveyed in this paper hopefully makes a contribution in an area where there is still an insufficient body of research and where additional exploration is needed. Findings: For two semesters rubrics were collected and analyzed representing the inclusion of 53 students. The target mean for the rubric was a 2.8 and the overall achieved mean was a 2.97, indicating that student performance met minimal expectations. Nevertheless, student deficiencies in three crucial areas were identified. According to the survey findings, as a result of the class students had a better understanding of the scientific method as it applies to the business discipline, are now better able to critically assess a problem, feel they can formulate a procedure to solve a problem, can test a problem-solving process, have a better understanding of how to formulate potential business solutions, understand how potential solutions are evaluated, and understand how business decisions are evaluated. Conclusion: Following careful consideration and discussion of the preliminary findings, the course under consideration was significantly enhanced. The changes were implemented in the fall of 2020 and initial data collected in the spring of 2021 is indicating measured improvement in student success as exhibited by higher rubric scores. Recommendations for Practitioners: These initial findings are promising and while considering student success, especially as we increasingly face a greater and greater portion of under-prepared students entering higher education, initiatives to build the higher order thinking skills of students via transdisciplinary courses may play an important role in the future of higher education. Recommendations for Researchers: Additional studies of transdisciplinary efforts to improve student outcomes need to be explored through collection and evaluation of rubrics used to assess student learning as well as by measuring student perception of the efficacy of these efforts. Impact on Society: Society needs more graduates who leave universities ready to solve problems critically, strategically, and with scientific reasoning. Future Research: This study was disrupted by the COVID-19 pandemic; however, it is resuming in late 2021 and it is the hope that a robust and detailed paper, with more expansive findings will eventually be generated.

Aim/Purpose; Fostering student engagement is one of the great challenges of teaching, especially in online learning environments. An educators’ assumptions and beliefs about what student engagement is and how it manifests will shape the strategies they design to engage students in learning. However, there is no agreement on the definition of concept of student engagement and it re-mains a vague construct. Background: Adopting the principles of user-centered design, the author maintains that to design learning experiences which better support student engagement it is important to gain insights into how students perceive and operationalize the concept of engagement in learning. The recent challenges of teaching effectively online prompted the author to reflect more deeply on the concept of engagement and how it might be achieved. Methodology: In the tradition of reflective teaching, the author undertook an informal, qualitative inquiry in her classroom, administering a brief questionnaire to students in her online class. When the themes which emerged were integrated with other literature and findings from the author’s earlier classroom inquiry, some insights were gained into how students ‘operationalize’ the concept of engagement, and weight was added to the authors’ premise of the value of humanistic approaches to university teaching, the need for greater emphasis on student-teacher connection and the necessity of considering the affective domain alongside the cognitive domain in learning in higher education. The insights were brought together and visualized in a conceptual model of student engagement. Contribution: The conceptual model presented in the present paper reflects the author’s present ‘mental model’ of student engagement in classes online and, when the opportunity arrives, in face-to-face classes as well. This mental model shapes the authors’ course design, learning activities and the delivery of the course. Although the elements of the model are not ‘new’, the model synthesizes several related concepts necessary to a humanist approach to under-standing student engagement. It is hoped that the model and discussion presented will be stimulus for further rich discussion around the nature of student engagement. Findings: Interestingly, the affective rather than the cognitive domain framed students’ perspectives on what engagement ‘looks like to them’ and on what teachers should do to engage them. Recommendations for Practitioners: By sharing the process through which the author arrived at this understanding of student engagement, the author has also sought to highlight three key points: the importance of including the ‘student perspectives and expectations’ against which educators can examine their own assumptions as part of the process reflective teaching practices; the usefulness of integrating theoretical and philosophical frameworks in our understandings of student engagement and how it might be nurtured, and finally the necessity of affording greater influence to humanism and the affective domain in higher education. The findings emphasize the necessity of considering the affective dimension of engagement as an essential condition for cognitive engagement and as inextricable from the cognitive dimension of engagement. Recommendations for Researchers: The emphasis in research engagement learning and teaching is on how we (the educators) can do this better, how we can better engage students. While the student perspective is often formulated from data obtained through surveys and focus groups, researchers in learning engagement are working with their own understandings (albeit supported by empirical research). It is crucial for deeper insight to also understand the students’ conceptualization of the phenomena being researched. Bringing the principles of design thinking to bear on educational research will likely provide greater depth of insight. Impact on Society: Empirical, formal, and structured research is undeniably essential to advancing human endeavor in any field, including learning and teaching. It is however important to recognize informal research in the form of classroom inquiry as part of teachers’ reflexive practice is also legitimate and useful to advancing understanding of complex phenomenon such as student engagement in learning through multiple perspectives and experiences. Future Research: Further research on the nature of student engagement in different contexts and against different theoretical frameworks is warranted as is empirical investigation of the premise of the value of humanism and the affective do-main in defining and measuring student engagement in higher education.

Aim/Purpose: Designing augmented reality (AR) experiences for education, health or entertainment involves multidisciplinary teams making design decisions across several areas. The goal of this paper is to present a classification schema that describes the design choices when constructing an AR interactive experience. Background: Existing extended reality schema often focuses on single dimensions of an AR experience, with limited attention to design choices. These schemata, combined with an analysis of a diverse range of AR applications, form the basis for the schema synthesized in this paper. Methodology: An extensive literature review and scoring of existing classifications were completed to enable a definition of seven design dimensions. To validate the design dimensions, the literature was mapped to the seven-design choice to represent opportunities when designing AR iterative experiences. Contribution: The classification scheme of seven dimensions can be applied to communicating design considerations and alternative design scenarios where teams of domain specialists need to collaborate to build AR experiences for a defined purpose. Findings: The dimensions of nature of reality, location (setting), feedback, objects, concepts explored, participant presence and interactive agency, and style describe features common to most AR experiences. Classification within each dimension facilitates ideation for novel experiences and proximity to neighbours recommends feasible implementation strategies. Recommendations for Practitioners: To support professionals, this paper presents a comprehensive classification schema and design rationale for AR. When designing an AR experience, the schema serves as a design template and is intended to ensure comprehensive discussion and decision making across the spectrum of design choices. Recommendations for Researchers: The classification schema presents a standardized and complete framework for the review of literature and AR applications that other researchers will benefit from to more readily identify relevant related work. Impact on Society: The potential of AR has not been fully realized. The classification scheme presented in this paper provides opportunities to deliberately design and evaluate novel forms of AR experience. Future Research: The classification schema can be extended to include explicit support for the design of virtual and extended reality applications.

Aim/Purpose: The aim of this paper is to analyze various text classification techniques employed in practice, their strengths and weaknesses, to provide an improved awareness regarding various knowledge extraction possibilities in the field of data mining. Background: Artificial Intelligence is reshaping text classification techniques to better acquire knowledge. However, in spite of the growth and spread of AI in all fields of research, its role with respect to text mining is not well understood yet. Methodology: For this study, various articles written between 2010 and 2017 on “text classification techniques in AI”, selected from leading journals of computer science, were analyzed. Each article was completely read. The research problems related to text classification techniques in the field of AI were identified and techniques were grouped according to the algorithms involved. These algorithms were divided based on the learning procedure used. Finally, the findings were plotted as a tree structure for visualizing the relationship between learning procedures and algorithms. Contribution: This paper identifies the strengths, limitations, and current research trends in text classification in an advanced field like AI. This knowledge is crucial for data scientists. They could utilize the findings of this study to devise customized data models. It also helps the industry to understand the operational efficiency of text mining techniques. It further contributes to reducing the cost of the projects and supports effective decision making. Findings: It has been found more important to study and understand the nature of data before proceeding into mining. The automation of text classification process is required, with the increasing amount of data and need for accuracy. Another interesting research opportunity lies in building intricate text data models with deep learning systems. It has the ability to execute complex Natural Language Processing (NLP) tasks with semantic requirements. Recommendations for Practitioners: Frame analysis, deception detection, narrative science where data expresses a story, healthcare applications to diagnose illnesses and conversation analysis are some of the recommendations suggested for practitioners. Recommendation for Researchers: Developing simpler algorithms in terms of coding and implementation, better approaches for knowledge distillation, multilingual text refining, domain knowledge integration, subjectivity detection, and contrastive viewpoint summarization are some of the areas that could be explored by researchers. Impact on Society: Text classification forms the base of data analytics and acts as the engine behind knowledge discovery. It supports state-of-the-art decision making, for example, predicting an event before it actually occurs, classifying a transaction as ‘Fraudulent’ etc. The results of this study could be used for developing applications dedicated to assisting decision making processes. These informed decisions will help to optimize resources and maximize benefits to the mankind. Future Research: In the future, better methods for parameter optimization will be identified by selecting better parameters that reflects effective knowledge discovery. The role of streaming data processing is still rarely explored when it comes to text classification.

Aim/Purpose: The clustering techniques are normally considered to determine the significant and meaningful subclasses purposed in datasets. It is an unsupervised type of Machine Learning (ML) where the objective is to form groups from objects based on their similarity and used to determine the implicit relationships between the different features of the data. Cluster Analysis is considered a significant problem area in data exploration when dealing with arbitrary shape problems in different datasets. Clustering on large data sets has the following challenges: (1) clusters with arbitrary shapes; (2) less knowledge discovery process to decide the possible input features; (3) scalability for large data sizes. Density-based clustering has been known as a dominant method for determining the arbitrary-shape clusters. Background: Existing density-based clustering methods commonly cited in the literature have been examined in terms of their behavior with data sets that contain nested clusters of varying density. The existing methods are not enough or ideal for such data sets, because they typically partition the data into clusters that cannot be nested. Methodology: A density-based approach on traditional center-based clustering is introduced that assigns a weight to each cluster. The weights are then utilized in calculating the distances from data vectors to centroids by multiplying the distance by the centroid weight. Contribution: In this paper, we have examined different density-based clustering methods for data sets with nested clusters of varying density. Two such data sets were used to evaluate some of the commonly cited algorithms found in the literature. Nested clusters were found to be challenging for the existing algorithms. In utmost cases, the targeted algorithms either did not detect the largest clusters or simply divided large clusters into non-overlapping regions. But, it may be possible to detect all clusters by doing multiple runs of the algorithm with different inputs and then combining the results. This work considered three challenges of clustering methods. Findings: As a result, a center with a low weight will attract objects from further away than a centroid with higher weight. This allows dense clusters inside larger clusters to be recognized. The methods are tested experimentally using the K-means, DBSCAN, TURN*, and IDCUP algorithms. The experimental results with different data sets showed that IDCUP is more robust and produces better clusters than DBSCAN, TURN*, and K-means. Finally, we compare K-means, DBSCAN, TURN*, and to deal with arbitrary shapes problems at different datasets. IDCUP shows better scalability compared to TURN*. Future Research: As future recommendations of this research, we are concerned with the exploration of further available challenges of the knowledge discovery process in clustering along with complex data sets with more time. A hybrid approach based on density-based and model-based clustering algorithms needs to compare to achieve maximum performance accuracy and avoid the arbitrary shapes related problems including optimization. It is anticipated that the comparable kind of the future suggested process will attain improved performance with analogous precision in identification of clustering shapes.

Aim/Purpose: Previous research has generally concentrated on identifying the variables that most significantly influence customer churn or has used customer segmentation to identify a subset of potential consumers, excluding its effects on forecast accuracy. Consequently, there are two primary research goals in this work. The initial goal was to examine the impact of customer segmentation on the accuracy of customer churn prediction in the banking sector using machine learning models. The second objective is to experiment, contrast, and assess which machine learning approaches are most effective in predicting customer churn. Background: This paper reviews the theoretical basis of customer churn, and customer segmentation, and suggests using supervised machine-learning techniques for customer attrition prediction. Methodology: In this study, we use different machine learning models such as k-means clustering to segment customers, k-nearest neighbors, logistic regression, decision tree, random forest, and support vector machine to apply to the dataset to predict customer churn. Contribution: The results demonstrate that the dataset performs well with the random forest model, with an accuracy of about 97%, and that, following customer segmentation, the mean accuracy of each model performed well, with logistic regression having the lowest accuracy (87.27%) and random forest having the best (97.25%). Findings: Customer segmentation does not have much impact on the precision of predictions. It is dependent on the dataset and the models we choose. Recommendations for Practitioners: The practitioners can apply the proposed solutions to build a predictive system or apply them in other fields such as education, tourism, marketing, and human resources. Recommendation for Researchers: The research paradigm is also applicable in other areas such as artificial intelligence, machine learning, and churn prediction. Impact on Society: Customer churn will cause the value flowing from customers to enterprises to decrease. If customer churn continues to occur, the enterprise will gradually lose its competitive advantage. Future Research: Build a real-time or near real-time application to provide close information to make good decisions. Furthermore, handle the imbalanced data using new techniques.