Miracle-dz/UniFAR-FTUs huggingface dataset:
Labels and low student engagement: How activity categorization impacts students’ perception and engagement of learning activities

LABELS AND LOW STUDENT ENGAGEMENT: HOW ACTIVITY CATEGORIZATION IMPACTS STUDENTS’ PERCEPTION AND ENGAGEMENT OF LEARNING ACTIVITIES Mohamed Hassan University of Guelph, 2019 Advisor: Dr. Benjamin Giguère Students’ engagement with learning activities and the type of attributions they make about their performance in these learning activities play a key role in their educational experience and success. The current Ontario high school applied and academic course curriculum is associated with lower educational success for students enrolled in the applied courses. This study used data from 213 undergraduate students to assess participants’ perception about the applied and academic courses and the impacts of applied/academic labels on students’ engagement and their performance attributions. A within-group design repeated measure ANOVA comparing the means of difficulty-level ratings of applied and academic courses indicated that participants perceived applied courses to be significantly less difficult than academic courses. The study did not, however, find any significant impact of the applied/academic label on participants’ engagement and attributions in online test activities labelled “applied” or “academic”.
Teacher Attrition: A Study of Teacher Attrition and Support in a County in West Virginia

The purpose of this study was to identify teacher attrition trends, the rationale for the attrition trends and evaluate the induction program in a school district in West Virginia. The goal of the study was to identify effective strategies to strengthen teacher support in the school district based on identified weaknesses from teachers within the district. The research questions identified how the attrition problem manifested within the district, reasons teachers left schools, and how the teacher induction and support systems aligned with the identified reasons for movement. Methods of data collection included quantitative data review of personnel records, semi-structured interviews of teachers who had worked in more than one building within the district, and an evaluation of the teacher induction program in the district. The interviews were recorded and analyzed to identify themes, which were shared with the evaluation group. The evaluation group analyzed the data and identified areas of strengths and weaknesses of the current support programs. The evaluation group made recommendations for change in the teacher support programs. Key findings of the study indicate that teachers need more support at their school level in terms of a school-based mentor and school-level administrator support. Another finding indicated that support needs to last longer than the first year of teaching within the district.
Innovative information literacy landscapes: Leveraging the specialized knowledge of LGBTQ+ communities in research and practice

This paper examines how Knowledge School principles can help libraries develop a more nuanced understanding of how social and cultural differences shape knowledge production and dissimination within LGBTQ+ communities. I focus on information literacy (IL), in which practitioners teach individuals to seek, evaluate, and use information. IL can empower communities by enhancing education, confidence, and decision-making. However, libraries often approach IL from a deficit, skills-based perspective by envisioning communities as lacking the requisite knowledge to fulfill their information needs. As a Knowledge School, we need to move away from one-size-fits-all approaches to librarianship. Through research, we can understand how communities produce and disseminate know-ledge on their terms. Such understanding can open up new, inclusive, and relevant possibilities for community-oriented practice. In this paper, I offer a lens through which to see how this occurs within a Knowledge School.
Analysis of Free-floating Bike Sharing and Insights on System Operations or Analyzing Mobility Patterns and Imbalance of Free Floating Bike Sharing Systems

Bike Sharing is a sustainable mode of urban mobility, not only for regular commuters but also for casual users and tourists. Freefloating bike sharing (FFBS) is an innovative bike sharing model, that saves on start-up cost, prevents bike theft, and offers significant opportunities for smart management by tracking bikes in real-time with built-in GPS. The primary objective of this paper is to understand the mobility patterns and imbalance of an FFBS by analyzing its historical trip and weather data. Resulting outcomes provide insights to assist the system operator to make more informed decisions. Researchers have studied mobility patterns by analyzing historical trip and weather data of station-based bike sharing systems (SBBS) using data visualization and or generalized linear models. However, none of these studies considered interaction between independent variables or study imbalance as a dependent variable. In this paper, we demonstrate that by considering such interactions, more insights can be obtained about the mobility patterns and imbalance of an FFBS. We propose a simple method to decompose continuous variables into binary variables and two stage models that consider interactions between independent variables. Abstract Bike Sharing is a sustainable mode of urban mobility, not only for regular commuters but also for casual users and tourists. Free-(cid:13)oating bike sharing (FFBS) is an innovative bike sharing model, that saves on start-up cost, prevents bike theft, and oﬀers signi(cid:12)cant opportunities for smart management by tracking bikes in real-time with built-in GPS. The primary objective of this paper is to understand the mobility patterns and imbalance of an FFBS by analyzing its historical trip and weather data. Resulting outcomes provide insights to assist the system operator to make more informed decisions. Researchers have studied mobility patterns by analyzing historical trip and weather data of station-based bike sharing systems (SBBS) using data visualization and or generalized linear models. However, none of these studies considered interaction between independent variables or study imbalance as a dependent variable. In this paper, we demonstrate that by considering such interactions, more insights can be obtained about the mobility patterns and imbalance of an FFBS. We propose a simple method to decompose continuous variables into binary variables and two stage models that consider interactions between independent variables. The proposed decomposition method signi(cid:12)cantly improves the (quasi-)Poisson regression model commonly used in the literature and has the ability to identify intervals of a continuous variable for which they are statistically signi(cid:12)cant.
Pengaruh Website STMIK Budi Darma Terhadap Penerimaan Mahasiswa Dengan Model TAM (Studi Kasus: Mahasiswa STMIK Budi Darma)

Website merupakan sebuah layanan internet yang memberi informasi kepada pengguna. Website STMIK Budi Darma sendiri memberikan informasi kepada seluruh masyarakat terutama untuk mahasiswa. Selama ini mahasiswa menggunakan website untuk memperoleh informasi seputar pengisian KRS dan KHS, informasi pengumuman, informasi jadwal kegiatan-kegiatan yang dilaksanakan di STMIK Budi Darma yang pastinya bermanfaat bagi mereka. Kemudahan Pengguna mengaplikasikan website dengan tampilan yang sederhana dan menarik menjadi salah satu hal yang paling penting bagi mahasiswa itu sendiri ketika menggunakannya baik melalui hand phone ataupun laptop. Tujuan dari penelitian dengan menggunakan model TAM (Technology Acceptance Model ) ini adalah untuk mengetahui apakah variable perceived usefulness (kemanfaatan) dan variabel perceived ease of use (kemudahan penggunaan) berpengaruh terhadap ( Attitude Toward Using )penerimaan sistem informasi website STMIK Budi Darma. Penelitian ini menggunakan responden sebanyak 70 mahasiswa STMIK Budi Darma dengan software SPSS versi 20.0. menggunakan hasil uji hipotesis yang terdiri atas uji Uji Regresi Linier Berganda, Uji Koefisien Determinasi (R 2 ) , Uji Partial (uji t) dan Uji Serempak (Uji F) Kata Kunci : TAM, perceived usefulness , perceived ease of use , Attitude Toward Using
How to maximize advertising performance in online social networks

Abstract The influence maximization problem is widely studied, but previous studies have assumed that the cost to activate each seed node was identical. In this paper, we consider different activation costs and investigate a new problem: the budget-aware influence maximization problem (BIM). This problem is NP-hard, which motivates our interest in its approximation. We develop two greedy algorithms for BIM, namely, BG and GMUI, and show that GMUI obtains a solution that is provably . Then, we consider the average profit to activate a common user and introduce GMUN, an improved GMUI algorithm. Finally, we evaluate our algorithms with experiments using two large real social networks. The results show that GMUI performs best in terms of influence, whereas GMUN creates leverage between the budget and net income, indicating that the marginal net income/costs ratio for each seed node selected by GMUN can satisfy enterprises.
DEVELOPMENT OF A PORTABLE HIGH PERFORMANCE MOBILE MAPPING SYSTEM USING THE ROBOT OPERATING SYSTEM

Abstract. The rapid progression in digitalization in the construction industry and in facility management creates an enormous demand for the efficient and accurate reality capturing of indoor spaces. Cloud-based services based on georeferenced metric 3D imagery are already extensively used for infrastructure management in outdoor environments. The goal of our research is to enable such services for indoor applications as well. For this purpose, we designed a portable mobile mapping research platform with a strong focus on acquiring accurate 3D imagery. Our system consists of a multi-head panorama camera in combination with two multi-profile LiDAR scanners and a MEMS-based industrial grade IMU for LiDAR-based online and offline SLAM. Our modular implementation based on the Robot Operating System enables rapid adaptations of the sensor configuration and the acquisition software. The developed workflow provides for completely GNSS-independent data acquisition and camera pose estimation using LiDAR-based SLAM. Furthermore, we apply a novel image-based georeferencing approach for further improving camera poses. First performance evaluations show an improvement from LiDAR-based SLAM to image-based georeferencing by an order of magnitude: from 10–13 cm to 1.3–1.8 cm in absolute 3D point accuracy and from 8–12 cm to sub-centimeter in relative 3D point accuracy.
High Availability Performance on OpenDayLight SDN Controller Platform (OSCP) Clustering and OpenDayLight with Heartbeat-Distributed Replicated Block Device (DRBD)

In this day people are asking for a reliable network when technology at its limit. Software-Defined Network (SDN) is an answer to that problem of network development where all the control over the network becomes centralized. However, all services controlled by a centralized controller have a big disadvantage if the controller dies. The High Availability (HA) is the solution. HA controller is divided into master and slave, when master controller is down then slave controller will respond to replace the function of master controller. In this research the system will be made by using two methods namely OpenDayLight SDN Controller Platform (OSCP) clustering and Heartbeat-DRBD (Distributed Replicated Block Device). OSCP clustering is a feature on OpenDayLight controller that is ready to be used and only need to be configured, with OSCP the main and backup controller clustering will be on connected cluster. Heartbeat-DRBD is an application commonly used to create High Availability systems on a server, but in this study will be used for the controller, Heartbeat will monitoring the main controller and if indicated to be down will move the resources to the backup controller with the DRBD application. From the simulation result shows that OSCP Clustering Failover and Failback average Time is 17 seconds while Heartbeat-DRBD is 23-45 seconds depends on how many switch and host are.While QoS parameters on both method have simillar value.it can be concluded that the High Availability system with OSCP Clustering method is more stable and good rather than Heartbeat-DRBD method to apply in a network.
Passion and enthusiasm: Exemplifying the place of disposition in teaching in New Zealand

Abstract There is an inconsistency between the importance of professional dispositions for teaching perceived by practitioners and other stakeholders in literature and the omission of dispositions in professional standards in New Zealand. Taking passion and enthusiasm as an example, this study explores the reasons for the inconsistency. Within the context of New Zealand early childhood education (ECE), interview data were collected from 21 practitioners and parents from seven ECE services and 15 teacher educators from eight universities and institutes of technology. Contextual, conceptual, and role-related factors are found to determine the teacher educators’ perceptions of the omission of passion and enthusiasm in the standards documents. “Conditional inclusion” and “contextual mediation” are proposed to negotiate a place in the government-imposed standards for passion and enthusiasm.
Infusing Service-Learning into Allied Health Profession Curriculum: Perceived Enablers and Barriers

Service-learning may offer an innovative perspective in the competing global and national health interests among Philippine universities. This study sought to understand the enablers and barriers of implementing service-learning in the context of a premier private Catholic Philippine allied health profession education. It used an in-depth descriptive qualitative design. Transcripts from interviews, field notes and organizational documents were analyzed using thematic analysis. Data were coded and categorized into perceived barriers and enablers according to The Self-Assessment Rubric for Institutionalization of Service-Learning in Higher Education (SRISL) by Furco (2002). A wide range of enablers and barriers to implementing SL in one of the university’s health-related program were discussed. The results of this study may support further uptake of service-learning as a signature pedagogy in the allied health profession, particularly for its emerging unique professional identity and role in enhancing disability-related health outcomes.
Pongo goes to school : an investigation into the role of school dogs in primary schools

Dogs have been used in therapeutic contexts since the 1960s, following Levinson’s pioneering success incorporating his dog ‘Jangles’ into psychotherapy sessions with children (Levinson, 1969). Dogs can be found in increasingly diverse social contexts, including prisons, hospitals, courtrooms, universities and also schools and there are bold claims that they serve to support humans through various processes of animal assisted therapy (AAT) and animal assisted learning (AAL). This study seeks to discover the role of school dogs and particularly in therapy and learning. The study is underpinned by literature and theoretical frameworks relating to AAT and AAL and to philosophical considerations of the human-animal divide and the human- animal bond. The interpretivist study follows a case study approach, with data generated from observations, interviews and scrutiny of associated documentary evidence. Data is presented using seven key themes. Six relate to AAT and are based upon the nurture model of Lucas, Insley and Buckland (2006) and the seventh theme relates to AAL. Findings point overwhelmingly towards AAT and to the facilitative role that school dogs play in nurture (Bowlby, 1956), with an indirect impact upon academic learning. New learning from this study indicates the positive impact of school dogs upon transition and school attendance, disclosure and safeguarding, communication, staff wellbeing and managing difficult parents.
Enhancing GPGPU Performance through Warp Scheduling, Divergence Taming and Runtime Parallelizing Transformations

There has been a tremendous growth in the use of Graphics Processing Units (GPU) for the acceleration of general purpose applications. The growth is primarily due to the huge computing power offered by the GPUs and the emergence of programming languages such as CUDA and OpenCL. A typical GPU consists of several 100s to a few 1000s of Single Instruction Multiple Data (SIMD) cores, organized as 10s of Streaming Multiprocessors (SMs), each having several SIMD cores which operate in a lock-step manner, offering a few TeraFLOPS of performance in a single socket. SMs execute instructions from a group of consecutive threads, called warps. At each cycle, an SM schedules a warp from a group of active warps and can context switch among the active warps to hide various stalls. However, various factors, such as global memory latency, divergence among warps of a thread block (TB), branch divergence among threads of a warp (Control Divergence), number of active warps, etc., can significantly impact the ability of a warp scheduler to hide stalls. This reduces the speedup of applications running on the GPU. Further, applications containing loops with potential cross iteration dependences, do not utilize the available resources (SIMD cores) effectively and hence suffer in terms of performance. In this thesis, we propose several mechanisms which address the above issues and enhance the performance of GPU applications through efficient warp scheduling, taming branch and warp divergence, and runtime parallelization. First, we propose RLWS, a Reinforcement Learning (RL) based Warp Scheduler which uses unsupervised learning to schedule warps based on the current state of the core and the long-term benefits of scheduling actions. As the design space involving the state variables used by the RL and the RL parameters (such as learning and exploration rates, reward and penalty values, etc.) is large, we use a Genetic Algorithm to identify the
A Data Management and Visualization Tool for Integrating Optimization Results in Product Development

This paper presents a data management and visualization tool that was developed in parallel with a Multidisciplinary Design Optimization (MDO) framework in order to enable a more effective use of the obtained results within the Product Development Process (PDP). To this date, the main problem is that the majority of MDO case studies conclude by suggesting a small number of optimal configurations, which do not really hold any meaningful value for the decision makers since they represent only a narrow area of the design space. In this light, the proposed tool aims to provide designers with new possibilities in respect to post-processing of large data sets, and subsequently, to allow the non-technical teams to be engaged and benefit from the use of MDO in the company practices. As an example, an Unmanned Aerial Vehicle (UAV) configurator developed by using the Graphical User Interface (GUI) of MATLAB is herein presented, and it is shown that a tool for handling the results can be the logical next step towards integrating MDO in the manufacturing industry. Overall, this work aims to demonstrate the benefits of the present visualization and management tool as a complementary addition to an existing optimization framework, and also to determine if this approach can be the right strategy towards improving the MDO method for an eventual use in the PDP of complex pro-ducts like UAVs.
The informal learning processes of young engineers at the workplace

The aim of the current study was to determine how young engineers learn at the workplace. Therefore, the research question is stated: How do young technical employees learn at the workplace? This research aims to make a contribution to existing theories about workplace learning, along with providing insights about the 21st century- and technical skills that needed to be learned, how these skills are learned and the transfer of these skills. This study is an exploratory qualitative research. Twenty young male technicians, between age 20 and 30, with a technical vocational educational background from different technological organisations from the eastern of the Netherlands, are interviewed. This research found out that, in terms of 21st century skills, especially thinking-, interpersonal-, and intrapersonal skills are learned at the workplace by the young engineers. In terms of technical skills, the young engineers learned to install or place specialist products, keep up with new technology and quality standards, and learned how to do work preparation.
Unexpected expectations: Public reaction to the Facebook emotional contagion study

How to ethically conduct online platform-based research remains an unsettled issue and the source of continued controversy. The Facebook emotional contagion study, in which researchers altered Facebook News Feeds to determine whether exposure to emotional content influences a user’s mood, has been one focal point of these discussions. The intense negative reaction by the media and public came as a surprise to those involved—but what prompted this reaction? We approach the Facebook study as a mediated controversy that reveals disconnects between how scholars, technologists, and the public understand platform-based research. We examine the controversy from the bottom up, analyzing public reactions expressed in comments on news articles. Our analysis reveals fundamental disagreements about what Facebook is and what a user’s relationship to it should be. We argue that these divergent responses emphasize the contextual nature of technology and research ethics, and conclude with a relational and contextual approach to ethical decision-making.
Longitudinal planning for personalized health management using daily behavioral data

Abstract Mitigating globally emerging health problems such as obesity needs scalable solutions that can facilitate health management and promote healthier lifestyles outside of clinical settings. Such scalable solutions, while targeting general population, need to provide personalized behavior change plans that not only fit users’ own underlying physiologic dynamics but also suit their preferences and needs. There has been fast-growing development of mobile health devices and applications for monitoring of human behavior (such as physical activity and food intake) and health status such as BMI. However, there are challenges to translate these noisy and dynamic behavioral data into personalized longitudinal planning. To address such challenges, we develop an integrated framework that unifies dynamic modeling, sparse learning, dictionary learning and matrix completion to translate users’ behavioral data into personalized dynamic system models and use them as constraints for deriving deeply personalized longitudinal health plans. We evaluate the proposed framework on a real-world user behavioral dataset and demonstrate its promising utility and efficacy.
Research and Practice of Bilingual Teaching Reform in Frontier Research of Functional Analysis

This paper introduces the research and practice of the frontier bilingual teaching reform of functional analysis, an innovative project of Shandong University of Technology. Firstly, the functional analysis is introduced, the problems to be solved in this research are put forward and the significance of reform research is discussed. Secondly, the contents and objectives of this project are arranged. Finally, the specific implementation scheme and time schedule are given.