Conceptual Analysis for Timely Social Media-Informed Personalized Recommendations

Integrating sensor networks and human social networks can provide rich data for many consumer applications. Conceptual analysis offers a way to reason about real-world concepts, which can assist in discovering hidden knowledge from the fused data. Knowledge discovered from such data can be used to provide mobile users with location-based, personalized and timely recommendations. Taking a multi-tier approach that separates concerns of data gathering, representation, aggregation and analysis, this paper presents a conceptual analysis framework that takes unified aggregated data as an input and generates semantically meaningful knowledge as an output. Preliminary experiments suggest that a fusion of sensor network and social media data improves the overall results compared to using either source of data alone

Student-Centered Learning: Functional Requirements for Integrated Systems to Optimize Learning

The realities of the 21st-century learner require that schools and educators fundamentally change their practice. "Educators must produce college- and career-ready graduates that reflect the future these students will face. And, they must facilitate learning through means that align with the defining attributes of this generation of learners."Today, we know more than ever about how students learn, acknowledging that the process isn't the same for every student and doesn't remain the same for each individual, depending upon maturation and the content being learned. We know that students want to progress at a pace that allows them to master new concepts and skills, to access a variety of resources, to receive timely feedback on their progress, to demonstrate their knowledge in multiple ways and to get direction, support and feedback from—as well as collaborate with—experts, teachers, tutors and other students.The result is a growing demand for student-centered, transformative digital learning using competency education as an underpinning.iNACOL released this paper to illustrate the technical requirements and functionalities that learning management systems need to shift toward student-centered instructional models. This comprehensive framework will help districts and schools determine what systems to use and integrate as they being their journey toward student-centered learning, as well as how systems integration aligns with their organizational vision, educational goals and strategic plans.Educators can use this report to optimize student learning and promote innovation in their own student-centered learning environments. The report will help school leaders understand the complex technologies needed to optimize personalized learning and how to use data and analytics to improve practices, and can assist technology leaders in re-engineering systems to support the key nuances of student-centered learning

IMPROVING THE DEPENDABILITY OF DESTINATION RECOMMENDATIONS USING INFORMATION ON SOCIAL ASPECTS

Prior knowledge of the social aspects of prospective destinations can be very influential in making travel destination decisions, especially in instances where social concerns do exist about specific destinations. In this paper, we describe the implementation of an ontology-enabled Hybrid Destination Recommender System (HDRS) that leverages an ontological description of five specific social attributes of major Nigerian cities, and hybrid architecture of content-based and case-based filtering techniques to generate personalised top-n destination recommendations. An empirical usability test was conducted on the system, which revealed that the dependability of recommendations from Destination Recommender Systems (DRS) could be improved if the semantic representation of social attributes information of destinations is made a factor in the destination recommendation process

Improving the Dependability of Destination Recommendations using Information on Social Aspects

Prior knowledge of the social aspects of prospective destinations can be very influential in making travel destination decisions, especially in instances where social concerns do exist about specific destinations. In this paper, we describe the implementation of an ontology-enabled Hybrid Destination Recommender System (HDRS) that leverages an ontological description of five specific social attributes of major Nigerian cities, and hybrid architecture of content-based and case-based filtering techniques to generate personalised top-n destination recommendations. An empirical usability test was conducted on the system, which revealed that the dependability of recommendations from Destination Recommender Systems (DRS) could be improved if the semantic representation of social attributes information of destinations is made a factor in the destination recommendation process.Content-based filtering; Recommender Systems; Ontology; Social Attributes, Destination recommendation

Digilego for Peripartum Depression: A Novel Patient-Facing Digital Health Instantiation

Digital health technologies offer unique opportunities to improve health outcomes for mental health conditions such as peripartum depression (PPD), a disorder that affects approximately 10-15% of women in the U.S. every year. In this paper, we present the adaption of a digital technology development framework, Digilego, in the context of PPD. Methods include mapping of the Behavior Intervention Technology (BIT) model and the Patient Engagement Framework (PEF) to translate patient needs captured through focus groups. This informs formative development and implementation of digital health features for optimal patient engagement in PPD screening and management. Results show an array ofPPD-specific Digilego blocks ( My Diary , Mom Talk , My Care , Library , How am I doing today? ). Initial evaluation results from comparative market analysis indicate that our proposed platform offers advantageous technology aspects. Limitations and future work in areas of interdisciplinary care coordination and patient engagement optimization are discussed

When artificial intelligence meets educational leaders’ data-informed decision-making: A cautionary tale

Artificial intelligence (AI) refers to a type of algorithms or computerized systems that resemble human mental processes of decision making. Drawing upon multidisciplinary literature that intersects AI, decision making, educational leadership, and policymaking, this position paper aims to examine promising applications and potential perils of AI in educational leaders’ data-informed decision making (DIDM). Endowed with ever-growing computational power and real-time data, highly scalable AI can increase efficiency and accuracy in leaders’ DIDM. However, misusing AI can have perilous effects on education stakeholders. Many lurking biases in current AI could be amplified. Of more concern, the moral values (e.g., fairness, equity, honesty, and doing no harm) we uphold might clash with using AI to make data-informed decisions. Further, missteps on the issues about data security and privacy could have a life-long impact on stakeholders. The article concludes with recommendations for educational leaders to leverage AI potential and minimize its negative consequences

A Normative Classification of Consumer Big Data

The big data phenomenon has transformed every area of life and business. Businesses today rely on the volume, velocity, and variety (3Vs) of data available today in product design, advertisement, sales, and post-sale follow up activities. Communication between the firm and the consumer is personalized using data collected on the consumer to match the consumer’s location, time, and needs. Some marketers argue that this has birth a new era of marketing; transformative marketing, in which the firm’s ability to deliver value and to acquire and maintain long-run competitive advantage determined by the firm’s data resources. In other words, data are the currency of the transformative marketing era. This sentiment is pervasive and has led to massive investments in data in recent years. This dissertation puts forward a classification of consumer big data to aid the firm extract value out of big data despite the 3Vs. The classification also demonstrates how value in a transformative marketing era does not have to be created at the expense of the consumer, but with the consumer. Five conceptual dichotomies are put forward in essay two that are more comprehensive than any other classification of data available in the research. Finally, the third essay investigates how the big data phenomenon affects consumer freedom and emotions. Most people agree that freedom is a fundamental human right, and that business practices should respect consumer freedom. However, research on consumer freedom is scant. Two experiments investigate how the characteristics of data collected on consumers affects consumer perception of decision freedom and satisfaction with value propositions. With the big data phenomenon has come a push toward algorithmic decision making. Consumer’s anxiety toward algorithmic decision making is investigated along with the satisfaction derived from decisions made by third parties that collect data on consumers

eCRM Features that Affect Customer Attitude to Loyalty: A Case Study of a Sample of 402 University Students Enrolled in International Programs in Thailand

The rapid growth of eCRM and its alarming failure rate call for a greater insight into the relationship between eCRM and its direct objective: customer satisfaction and customer loyalty. The purpose of this research is to give a better understanding of customer perception of value from eCRM features on companies’ websites. In this study, the researcher empirically tested a model explaining the relationship between three eCRM features, (i.e. personalization, community and convenience), customer satisfaction and customer loyalty. The empirical data were collected from 402 customers through a survey questionnaire. Findings of this study showed all the features of eCRM influenced customer satisfaction significantly, at the same time a significant impact of personalization and convenience on customer loyalty was found. The results also indicated a moderate effect of customer satisfaction on customer loyalty. This research clarifies the role of eCRM features in enhancing customer loyalty directly and through customer satisfaction; also highlights the critical features of eCRM program which companies’ websites should invest in their customer loyalty strategies

Exploring the future of mathematics teaching: Insight with ChatGPT

This study aims to provide a comprehensive overview of the future of mathematics teaching from the perspective of ChatGPT, an advanced language processing artificial intelligence (AI) developed by OpenAI. The results of the chat transcripts edited with ChatGPT suggest that the future of mathematics teaching will see the integration of technology and AI to provide personalized learning experiences, blended learning environments, and computational thinking, data literacy, and statistics. Problem-solving, critical thinking, and interdisciplinary connections will continue to be emphasized, and equity and inclusion will remain crucial. AI is expected to revolutionize mathematics education, but thoughtful implementation, ongoing professional development, and pedagogical considerations are essential. However, the future of teaching mathematics will continue to evolve. Therefore, teachers and lecturers need to keep abreast of the latest developments and adapt to them while remaining committed to providing quality teaching.Studi ini bertujuan untuk memberikan gambaran komprehensif tentang masa depan pengajaran matematika dari perspektif ChatGPT, Artificial Intelligence (AI) pemrosesan bahasa tingkat lanjut yang dikembangkan oleh OpenAI. Hasil transkrip obrolan yang diedit dengan ChatGPT menunjukkan bahwa masa depan pengajaran matematika akan melihat integrasi teknologi dan AI untuk memberikan pengalaman belajar yang dipersonalisasi, lingkungan pembelajaran campuran, dan pemikiran komputasi, literasi data, dan statistik. Pemecahan masalah, pemikiran kritis, dan koneksi interdisipliner akan terus ditekankan, dan kesetaraan dan inklusi akan tetap penting. AI diharapkan merevolusi pendidikan matematika, tetapi implementasi yang bijaksana, pengembangan profesional berkelanjutan, dan pertimbangan pedagogis sangat penting. Namun, masa depan pengajaran matematika akan terus berkembang. Oleh karena itu, guru dan dosen perlu mengikuti perkembangan terkini dan beradaptasi dengannya sambil tetap berkomitmen untuk memberikan pengajaran yang berkualitas