An E-Portfolio Assessment Management System (EAMS) can be an innovative tool that provides students with flexible opportunities to demonstrate the acquisition of skills and abilities in an outcome-based institution. The system has been developed and used for the past ten years to create, reflect, revise, and structure students’ work. It is a repository management system that facilitates collecting, sharing, and presenting artifacts of student learning outcomes via a digital medium. Therefore, it provides students with flexible opportunities to demonstrate the acquisition of skills and abilities to demonstrate growth of achieving learning outcomes. The rationale of the EAMS is to allow students to demonstrate competences and reflect upon experiences to improve their learning and career readiness; hence, they are accountable for their learning. The system was built around two defined set of learning outcomes: institutionally agreed upon set of learning outcomes, and learning objectives that are related to major requirements. The purpose of this study is to analyze students’ perceptions and attitudes when using an e-portfolio to support their employment opportunities. The participants were 217 students in the College of Technological Innovation. The students reported that the developing of e-portfolios was extremely helpful. The results showed that students have positive opinions about using e-portfolios as a beneficial tool to support their readiness for employment; they believe an e-portfolio increases their confidence to find a job in the IT field because it can allow them to showcase artifacts that demonstrate competencies and reflect upon experiences, and they can provide their supervisors during their industrial training with an e-resume that includes views of their actual work of what they have learned and are able to do when they complete their degree. Employers then can review e-portfolios to select prospective employees work readiness skills; hence, graduates are more likely to obtain a job in their workplaces. In conclusion, students do like the idea of e-portfolios when it is presented to them as a career showcase rather than a process for documenting learning. A career center can use e-portfolios as a tool to help students find a job. Furthermore, our analysis and evaluation uncovered learning issues involved in moving from the traditional approach of learning toward an integrated learning system that can be used after graduation.

This paper will shed light on the lack of the development of appropriate monitoring systems in the field of education. Test banks can be easily purchased. Smart phones can take and share pictures of exams. A video of an exam given through Blackboard can easily be made. A survey to determine the extent of cheating using technology was given to several university students. Evidence is provided that shows security is lacking as evidenced by the number of students who have made use of technological advances to cheat on exams. The findings and conclusion may serve as evidence for administrators and policy makers to re-assess efforts being made to increase security in online testing.

Health Data collection is one of the major components of public health systems. Decision makers, policy makers, and medical service providers need accurate and timely data in order to improve the quality of health services. The rapid growth and use of mobile technologies has exerted pressure on the demand for mobile-based data collection solutions to bridge the information gaps in the health sector. We propose a prototype using open source data collection frameworks to test its feasibility in improving the vaccination data collection in Kenya. KenVACS, the proposed prototype, offers ways of collecting vaccination data through mobile phones and visualizes the collected data in a web application; the system also sends reminder short messages service (SMS) to remind parents on the date of the next vaccination. Early evaluation demonstrates the benefits of such a system in supporting and improving vaccination of children. Finally, we conducted a qualitative study to assess challenges in remote health data collection and evaluated usability and functionality of KenVACS.

Aim/Purpose: The topic of this study is the ICT-enabled transformation of car dealerships to regional providers of sustainable mobility (e.g., car sharing). Background: Car dealerships offer specific conditions that enable a sustainable mobility offer, based on individual motorized transport like car sharing. This is especially useful in small towns or rural areas where people’s mobility is strongly dominated by private cars, and public transport coverage is limited. However, these new mobility services are combined services with the need of a deep integration of information systems, and these services are not yet related to car dealerships and customer acquisition has to be supported. Methodology: An empirical study with an inductive approach was chosen. The study consists of interviews with three focus groups of different stakeholders of car dealerships. Within the frame of the research project, “ReCaB – Regional Car-Balancing” a qualitative research approach was chosen. Within a design science approach the existing SusCRM architecture was adapted based on the elaborated requirements. Contribution: A software architecture is proposed, where Customer Relationship Management (CRM) components to market new sustainable mobility offers are vital parts and existing information systems of car dealerships are integrated. Findings: The basic feasibility of the establishment and customer acceptance has been demonstrated, at least in the area of car sharing within ReCaB. The execution in the car sharing field has already started and a number of car dealerships are already bringing their own offers to market. Major findings for the SusCRM architecture have been elaborated in a design science approach in the national German research project “Showcase for electro mobility”. Recommendations for Practitioners: There is still no fully functional prototype developed for this specific use case and evaluated in the car dealership environment. An implementation only, based on own efforts, is difficult for car dealerships because of the dealership’s lack of know-how as well as tight IT budgets. However, this approach appears particularly successful in rural areas where public transport services are heavily declining. For full implementation, the presented ICT support is imperative. Recommendation for Researchers: The research on the retailer level, especially in combination with a digital trans-formation by the use of ICT systems, is still fragmentary. Research in this area that both addresses sustainability goals on a general level and supports economical goals on the company level of automotive retailers would be useful. Impact on Society: Creation of sustainable offers as a substitute or supplement for mobility based on their own car is a promising way to reduce negative effects of mobility. Enhancement of the mobility of the people in rural areas is leading to a more active lifestyle by reaching the leisure facilities, workplaces and educational institutions in a sustainable manner. Future Research: In terms of the fast changing mobility landscape, especially on a technical level with the development of autonomous vehicles and digitalization of entire businesses new solutions are becoming accessible, that have to be integrated in further research.

Aim/Purpose: In this article, we provide a better solution to Webpage access prediction. In particularly, our core proposed approach is to increase accuracy and efficiency by reducing the sequence space with integration of PageRank into CPT+. Background: The problem of predicting the next page on a web site has become significant because of the non-stop growth of Internet in terms of the volume of contents and the mass of users. The webpage prediction is complex because we should consider multiple kinds of information such as the webpage name, the contents of the webpage, the user profile, the time between webpage visits, differences among users, and the time spent on a page or on each part of the page. Therefore, webpage access prediction draws substantial effort of the web mining research community in order to obtain valuable information and improve user experience as well. Methodology: CPT+ is a complex prediction algorithm that dramatically offers more accurate predictions than other state-of-the-art models. The integration of the importance of every particular page on a website (i.e., the PageRank) regarding to its associations with other pages into CPT+ model can improve the performance of the existing model. Contribution: In this paper, we propose an approach to reduce prediction space while improving accuracy through combining CPT+ and PageRank algorithms. Experimental results on several real datasets indicate the space reduced by up to between 15% and 30%. As a result, the run-time is quicker. Furthermore, the prediction accuracy is improved. It is convenient that researchers go on using CPT+ to predict Webpage access. Findings: Our experimental results indicate that PageRank algorithm is a good solution to improve CPT+ prediction. An amount of though approximately 15 % to 30% of redundant data is removed from datasets while improving the accuracy. Recommendations for Practitioners: The result of the article could be used in developing relevant applications such as Webpage and product recommendation systems. Recommendation for Researchers: The paper provides a prediction model that integrates CPT+ and PageRank algorithms to tackle the problem of complexity and accuracy. The model has been experimented against several real datasets in order to show its performance. Impact on Society: Given an improving model to predict Webpage access using in several fields such as e-learning, product recommendation, link prediction, and user behavior prediction, the society can enjoy a better experience and more efficient environment while surfing the Web. Future Research: We intend to further improve the accuracy of webpage access prediction by using the combination of CPT+ and other algorithms.

Aim/Purpose: Although the significance of data provenance has been recognized in a variety of sectors, there is currently no standardized technique or approach for gathering data provenance. The present automated technique mostly employs workflow-based strategies. Unfortunately, the majority of current information systems do not embrace the strategy, particularly biodiversity information systems in which data is acquired by a variety of persons using a wide range of equipment, tools, and protocols. Background: This article presents an automated technique for producing temporal data provenance that is independent of biodiversity information systems. The approach is dependent on the changes in contextual information of data items. By mapping the modifications to a schema, a standardized representation of data provenance may be created. Consequently, temporal information may be automatically inferred. Methodology: The research methodology consists of three main activities: database event detection, event-schema mapping, and temporal information inference. First, a list of events will be detected from databases. After that, the detected events will be mapped to an ontology, so a common representation of data provenance will be obtained. Based on the derived data provenance, rule-based reasoning will be automatically used to infer temporal information. Consequently, a temporal provenance will be produced. Contribution: This paper provides a new method for generating data provenance automatically without interfering with the existing biodiversity information system. In addition to this, it does not mandate that any information system adheres to any particular form. Ontology and the rule-based system as the core components of the solution have been confirmed to be highly valuable in biodiversity science. Findings: Detaching the solution from any biodiversity information system provides scalability in the implementation. Based on the evaluation of a typical biodiversity information system for species traits of plants, a high number of temporal information can be generated to the highest degree possible. Using rules to encode different types of knowledge provides high flexibility to generate temporal information, enabling different temporal-based analyses and reasoning. Recommendations for Practitioners: The strategy is based on the contextual information of data items, yet most information systems simply save the most recent ones. As a result, in order for the solution to function properly, database snapshots must be stored on a frequent basis. Furthermore, a more practical technique for recording changes in contextual information would be preferable. Recommendation for Researchers: The capability to uniformly represent events using a schema has paved the way for automatic inference of temporal information. Therefore, a richer representation of temporal information should be investigated further. Also, this work demonstrates that rule-based inference provides flexibility to encode different types of knowledge from experts. Consequently, a variety of temporal-based data analyses and reasoning can be performed. Therefore, it will be better to investigate multiple domain-oriented knowledge using the solution. Impact on Society: Using a typical information system to store and manage biodiversity data has not prohibited us from generating data provenance. Since there is no restriction on the type of information system, our solution has a high potential to be widely adopted. Future Research: The data analysis of this work was limited to species traits data. However, there are other types of biodiversity data, including genetic composition, species population, and community composition. In the future, this work will be expanded to cover all those types of biodiversity data. The ultimate goal is to have a standard methodology or strategy for collecting provenance from any biodiversity data regardless of how the data was stored or managed.

Aim/Purpose: This study presents a novel deep learning-based framework designed to enhance spontaneous micro-expression recognition by effectively increasing the amount and variety of data and balancing the class distribution to improve recognition accuracy. Background: Micro-expression recognition using deep learning requires large amounts of data. Micro-expression datasets are relatively small, and their class distribution is not balanced. Methodology: This study developed a framework using a deep learning-based model to recognize spontaneous micro-expressions on a person’s face. The framework also includes several technical stages, including image and data preprocessing. In data preprocessing, data augmentation is carried out to increase the amount and variety of data and class balancing to balance the distribution of sample classes in the dataset. Contribution: This study’s essential contribution lies in enhancing the accuracy of micro-expression recognition and overcoming the limited amount of data and imbalanced class distribution that typically leads to overfitting. Findings: The results indicate that the proposed framework, with its data preprocessing stages and deep learning model, significantly increases the accuracy of micro-expression recognition by overcoming dataset limitations and producing a balanced class distribution. This leads to improved micro-expression recognition accuracy using deep learning techniques. Recommendations for Practitioners: Practitioners can utilize the model produced by the proposed framework, which was developed to recognize spontaneous micro-expressions on a person’s face, by implementing it as an emotional analysis application based on facial micro-expressions. Recommendation for Researchers: Researchers involved in the development of a spontaneous micro-expression recognition framework for analyzing hidden emotions from a person’s face are playing an essential role in advancing this field and continue to search for more innovative deep learning-based solutions that continue to explore techniques to increase the amount and variety of data and find solutions to balancing the number of sample classes in various micro-expression datasets. They can further improvise to develop deep learning model architectures that are more suitable and relevant according to the needs of recognition tasks and the various characteristics of different datasets. Impact on Society: The proposed framework could significantly impact society by providing a reliable model for recognizing spontaneous micro-expressions in real-world applications, ranging from security systems and criminal investigations to healthcare and emotional analysis. Future Research: Developing a spontaneous micro-expression recognition framework based on spatial and temporal flow requires the learning model to classify optimal features. Our future work will focus more on exploring micro-expression features by developing various alternative learning models and increasing the weights of spatial and temporal features.

Breast cancer is leading cause of mortality among women compared to other types of cancers. Hence, early breast cancer diagnosis is crucial to the success of treatment. Various pathological and imaging tests are available for the diagnosis of breast cancer. However, it may introduce errors during detection and interpretation, leading to false-negative and false-positive results due to lack of pre-processing of it. To overcome this issue, we proposed a effective image pre-processing technique-based on Otsu's thresholding and single-seeded region growing (SSRG) to remove artefacts and segment the pectoral muscle from breast mammograms. To validate the proposed method, a publicly available MIAS dataset was utilised. The experimental finding showed that proposed technique improved 18% breast cancer detection accuracy compared to existing methods. The proposed methodology works efficiently for artefact removal and pectoral segmentation at different shapes and nonlinear patterns.

In the context of asthma severity prediction, this study delves into the feature importance of various symptoms and demographic attributes. Leveraging a comprehensive dataset encompassing symptom occurrences across varying severity levels, this investigation employs visualisation techniques, such as stacked bar plots, to illustrate the distribution of symptomatology within different severity categories. Additionally, correlation coefficient analysis is applied to quantify the relationships between individual attributes and severity levels. Moreover, the study harnesses the power of random forest and the Gini importance methodology, essential tools in feature importance analytics, to discern the most influential predictors in asthma severity prediction. The experimental results bring to light compelling associations between certain symptoms, notably 'runny-nose' and 'nasal-congestion', and specific severity levels, elucidating their potential significance as pivotal predictive indicators. Conversely, demographic factors, encompassing age groups and gender, exhibit comparatively weaker correlations with symptomatology. These findings underscore the pivotal role of individual symptoms in characterising asthma severity, reinforcing the potential for feature importance analysis to enhance predictive models in the realm of asthma management and bioinformatics.

Aim/Purpose: The purpose of this paper is to introduce, describe, and document the methods involved in the preparation of a mindset intervention built into a freshmen development course, and established after years of longitudinal research, that is designed to have a positive impact on the outlook, achievement, and persistence of first generation and under-prepared students. Background: A number of studies conducted in the past fifteen years have concluded that grit, the persistence and perseverance to achieve goals, and growth mindset, the belief that skills and intelligence can be developed, are positive predictors of achievement; however, little focus has been placed on the implications at institutions purposed to educate minorities, first generation college students, and learners from diminished socio-economic backgrounds. Methodology: A series of models were created, custom self-assessment scales designed, and a lesson plan prepared purposed to deliver a mindset intervention to edify students about and change perceptions of grit, locus of control/self-efficacy, growth mindset, and goal setting. The mindset intervention, as presented in this paper, was delivered as part of a pilot implementation to students enrolled in a freshmen professional development course at a Mid-Atlantic HBCU in the Fall of 2019. Contribution: This qualitative paper documents an ongoing initiative while providing a workable template for the design and delivery of a mindset intervention that is believed will be highly effective with first generation and socio-economically disadvantaged learners. It represents the third paper in a five paper series. Findings: Prior research conducted by the authors shed light on the need to explore non-cognitive factors that may affect student performance such as grit, mindset, engagement, self-efficacy, and goal setting. The authors postulate that a carefully crafted mindset intervention delivered to freshmen students from traditionally underserved populations attending a minority serving institution in the mid-atlantic region of the United States will yield positive outcomes in terms of student success. Recommendations for Practitioners: As part of a commitment to positive student outcomes, faculty and administrators in higher education must be constantly exploring factors that may, or may not, impact student success. Recommendation for Researchers: Research is needed that explores elements that may help to contribute to the success of under prepared college students, in particular those who are from low income, first generation, and minority groups Impact on Society: Since, mindset interventions have been shown to be particularly effective with underserved students, it stands to reason that they should be adopted widely, and be effective at delivering positive outcomes, at HBCUs Future Research: The authors have introduced the mindset intervention with freshmen business students enrolled in a required professional development course. Results of the self-assessments and reflection questions are being collected and coded. Additionally, students are being administered a survey designed to measure the perceived efficacy of the initiative.

Aim/Purpose Information Technology students’ learning outcomes improve when teaching methodology moves away from didactic behaviorist-based pedagogy toward a more heuristic constructivist-based version of andragogy. Background There is a distinctive difference, a notable gap, between the academic community and the business community in their views of the level of preparedness of recent information technology program graduates. Understanding how Information Technology curriculum is developed and taught along with the underpinning learning theory is needed to address the deficient attainment of learning outcomes at the heart of this matter. Methodology The case study research methodology has been selected to conduct the inquiry into this phenomenon. This empirical inquiry facilitates exploration of a contemporary phenomenon in depth within its real-life context using a variety of data sources. The subject of analysis will be two Information Technology classes composed of a combination of second year and third year students; both classes have six students, the same six students. Contribution It is the purpose of this research to show that the use of improved approaches to learning will produce more desirable learning outcomes. Findings The results of this inquiry clearly show that the use of the traditional behaviorist based pedagogic model to achieve college and university IT program learning outcomes is not as effective as a more constructivist based andragogic model. Recommendations Instruction based purely on either of these does a disservice to the typical college and university level learner. The correct approach lies somewhere in between them; the most successful outcome attainment would be the product of incorporating the best of both. Impact on Society Instructional strategies produce learning outcomes; learning outcomes demonstrate what knowledge has been acquired. Acquired knowledge is used by students as they pursue professional careers and other ventures in life. Future Research Learning and teaching approaches are not “one-size-fits-all” propositions; different strategies are appropriate for different circumstances and situations. Additional research should seek to introduce vehicles that will move learners away from one the traditional methodology that has been used throughout much of their educational careers to an approach that is better suited to equip them with the skills necessary to meet the challenges awaiting them in the professional world.

Aim/Purpose: With information almost effortlessly created and spontaneously available, current progress in Information and Communication Technology (ICT) has led to the complication that information must be scrutinized for trustworthiness and provenance. Information systems must become provenance-aware to be satisfactory in accountability, reproducibility, and trustworthiness of data. Background: Multiple models for abstract representation of provenance have been proposed to describe entities, people, and activities involved in producing a piece of data, including the Open Provenance Model (OPM) and the World Wide Web Consortium. These models lack certain concepts necessary for specifying workflows and encoding the provenance of data products used and generated. Methodology: Without loss of generality, the focus of this paper is on OPM depiction of provenance in terms of a directed graph. We have redrawn several case studies in the framework of our proposed model in order to compare and evaluate it against OPM for representing these cases. Contribution: This paper offers an alternative flow-based diagrammatic language that can form a foundation for modeling of provenance. The model described here provides an (abstract) machine-like representation of provenance. Findings: The results suggest a viable alternative in the area of diagrammatic representation for provenance applications. Future Research: Future work will seek to achieve more accurate comparisons with current models in the field.

Aim/Purpose: This paper describes an empirical examination of how users’ willingness to disclose personal data is influenced by the amount of information provided on landing pages – standalone web pages created explicitly for marketing or advertising campaigns. Background: Provision of information is a central construct in the IS discipline. Content is a term commonly used to describe the information made available by a website or other electronic medium. A pertinent debate among scholars and practitioners relate to the behavioral impact of content volume: Specifically, does a greater amount of information elicit engagement and compliance, or the other way around? Methodology: A series of large-scale web experiments (n= 535 and n= 27,900) were conducted employing a between-subjects design and A/B testing. Two variants of landing pages, long and short, were created based on relevant behavioral theories. Both variants included an identical form to collect users’ information, but different amounts of provided content. User traffic was generated using Google AdWords and randomized between the page using Unbounce.com. Relevant usage metrics, such as response rate (called “conversion rate”), location, and visit time were recorded. Contribution: This research contributes to the body of knowledge on information provision and its effectiveness and carries practical and theoretical implications to practitioners and scholars in Information Systems, Informing Science, Communications, Digital Marketing, and related fields. Findings: Analyses of results show that the shorter landing pages had significantly higher conversion rates across all locations and times. Findings demonstrate a negative correlation between the content amount and consumer behavior, suggesting that users who had less information were more inclined to provide their data. Recommendations for Practitioners: At a practical level, results can empirically support business practices, design considerations, and content strategy by informing practitioners on the role of content in online commerce. Recommendation for Researchers: Findings suggest that the amount of content plays a significant role in online decision making and effective informing. They also contradict prior research on trust, persuasion, and security. This study advances research on the paradoxical relationship between the increased level of information and online decision-making and indicates that contrary to earlier work, not all persuasion theories‎ are ‎effective online. Impact on Society: Understanding how information drives behavior has implications in many domains (civic engagement, health, education, and more). This has relevance to system design and public communication in both online and offline contexts. Future Research: Using this research as a starting point, future research can examine the impact of content in other contexts, as well as other behavioral drivers (such as demographic data). This can lead to theoretical, methodological, and practical recommendations.

Previous academic and popular literature has raised important debates concerning the contradictory incentives of international firms to reduce their environmental impacts and offer transparent environmental information about their operations. As an exhaustive review of this literature reveals mixed and partial evidence, we compared the individual corporate environmental performance and disclosure of the 100 most international non-financial firms in the world to those of 16,023 firms in their industries and a group of matched pairs of firms for three different years. Our results show that although the top international firms have a much better record of environmental disclosure than the firms within their industries and the matched pairs, the top international firms also show worse environmental performance than their peers. The results suggest that the top international firms seek legitimation for their environmental activities by means of voluntary disclosure.

This rather difficult read introduces the programming language FSM and the programming platform DrRacket. The author asserts that it is a convenient platform to design and prove an automata-based software

Improving equity in data science, edited by Colby Tofel-Grehl and Emmanuel Schanzer, is a thought-provoking exploration of how data science education can be transformed to foster equity, especially within K-16 classrooms. The editors advocate for redefining

In the initial six months, Khurshid’s relatively inexperienced government faced some tough challenges.

Special training for officers and provision of armoured vehicles were key directives from the Punjab CM today

Having passed the mid-year point, it is always an interesting exercise to take stock of where we stand with respect to the approval of new medicines. Overall, when considering the volume of activity, things would seem to be looking up … Continue reading →

Alberto Fujimori leaves behind a mixed legacy of growth and controversy after battling cancer

Efter et anonymt tip om Den Internationale Domstols igangværende undersøgelser om formodet Folkedrab begået af Israel har EBU øjeblikkeligt udelukket den israelske deltager fra at deltage i aftenens finale. 

Indlægget Israel smidt ud af Eurovision: Anklager om folkedrab i strid med konkurrencereglerne blev først udgivet på RokokoPosten.

TomTom to Provide Geocoding for Hotel Search Platform trivago

More than 400 years after her death, the truth about "the Blood Countess," a Hungarian noblewoman alleged to have been the most prolific female serial killer of all time, remains elusive.

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Devin Carter scored 11 of his 29 points in the last two minutes, including seven in a row as Providence came from behind to give former coach Ed Cooley a hostile welcome back and beat Georgetown 84-76 on Saturday.

The White House should not overlook the complex opportunities and challenge of space now and in the future, according to a new "Presidential Transition: Priority Topic Memo" released by Mitre, a nonprofit group established in 1958 and focused on national security, aerospace, artificial intelligence and more.

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Last season, Maryland beat Indiana so bad that the Hoosiers fired their offensive coordinator less than 24 hours later. How much things can change in just one year, as Indiana is at 4-0 in 2024 as they await the Terrapins on Saturday.

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A new "LinkOut" feature introduced by the National Center for Biotechnology Information (NCBI) NCBI’s allows the easy linking to content on PubMed and GenBank.  Dryad has already introduced the feature benefitting from easy and fast linking of associated content to the two resources.

PubMed and GenBank, from the National Center for Biotechnology Information (NCBI), are hugely popular resources for searching and retrieving article abstracts and nucleotide sequence data, respectively.  PubMed indexes the vast majority of the biomedical literature, and deposition of nucleotide sequences in GenBank or one of the other INSDC databases is a near universal requirement for publication in a scientific journal. LinkOut allows the data from an article to be distributed among repositories without compromising its discoverability.

Dryad, intends to expand on this feature in a couple of ways. First, it is planned to make Dryad content searchable via the PubMed and GenBank identifiers, which because of their wide use will provide a convenient gateway for other biomedical databases to link out to Dryad.  Second, open web standards will be used to expose relationships between content in Dryad and other repositories, not just NCBI.

Original source: Dryad news and views