my.Eskwela: Designing An Enterprise Learning Management System to Increase Social Network and Reduce Cognitive Load

A typical learning management system (LMS) provides a tool for teachers to upload and create links to resources, create online assessments and provide immediate evaluation to students. As much as it tries to be student centered, most LMS remains a tool for instruction rather than learning. In a learning generation that is bound by very high online social capital, connectedness to the family weakens. my.Eskwela (My School) redefines LMS to include a parent component to address the need for inclusive participation of parents in the teaching-learning process. Basis for re-design came from the low user acceptance of teachers in using similar system. The study premised that designing an environment that evokes a ”feeling of socialness” through social widgets provides a perceived presence of a social environment that will increase usage of the system. In a majority of the focus group discussion, results showed a more positive evaluation of the system. Precisely, for perceived usefulness, perceived ease of use, perceived adoption and intent to use, it can be reasoned that the implementations for reducing the total effort to perform a task and the effect of implementing social interaction in the user-interface has high-impact

A Multi-model Approach in Developing an Intelligent Assistant for Diagnosis Recommendation in Clinical Health Systems

Clinical health information systems capture massive amounts of unstructured data from various health and medical facilities. This study utilizes unstructured patient clinical text data to develop an intelligent assistant that can identify possible related diagnoses based on a given text input. The approach applies a one-vs-rest binary classification technique wherein given an input text data, it is identified whether it can be positively or negatively classified for a given diagnosis. Multi-layer Feed-Forward Neural Network models were developed for each individual diagnosis case. The task of the intelligent assistant is to iterate over all the different models and return those that output a positive diagnosis. To validate the performance of the models, the performance metrics were compared against Naive Bayes, Decision Trees, and K-Nearest Neighbor. The results show that the neural network learner provided better performance scores in both accuracy and area under the curve metric scores. Further, testing on multiple diagnoses also shows that the methodology for developing the diagnosis models can be replicated for development of models for other diseases as well

Infodemiology for Syndromic Surveillance of Dengue and Typhoid Fever in the Philippines

Finding determinants of disease outbreaks before its occurrence is necessary in reducing its impact in populations. The supposed advantage of obtaining information brought by automated systems fall short because of the inability to access real-time data as well as interoperate fragmented systems, leading to longer transfer and processing of data. As such, this study presents the use of realtime latent data from social media, particularly from Twitter, to complement existing disease surveillance efforts. By being able to classify infodemiological (health-related) tweets, this study is able to produce a range of possible disease incidences of Dengue and Typhoid Fever within the Western Visayas region in the Philippines. Both diseases showed a strong positive correlation (R \u3e .70) between the number of tweets and surveillance data based on official records of the Philippine Health Agency. Regression equations were derived to determine a numerical range of possible disease incidences given certain number of tweets. As an example, the study shows that 10 infodemiological tweets represent the presence of 19-25 Dengue Fever incidences at the provincial level

Towards developing an intelligent agent to assist in patient diagnosis using neural networks on unstructured patient clinical notes: Initial analysis and models

Technological advances in information-communication technologies in the health ecosystem have allowed for the recording and consumption of massive amounts of structured and unstructured health data. In developing countries, the use of Electronic Medical Records (EMR) is necessary to address the need for efficient delivery of services and informed decision-making, especially at the local level where health facilities and practitioners may be lacking. Text mining is a variation of data mining that tries to extract non-trivial information and knowledge from unstructured text. This study aims to determine the feasibility of integrating an intelligent agent within EMRs for automatic diagnosis prediction based on the unstructured clinical notes. A Multilayer Feed- Forward Neural Network with Back Propagation training was implemented for classification. The two neural network models predicted hypertension against similar diagnoses with 11.52% and 10.53% percent errors but predicted with 54.01% and 64.82% percent errors when used on a group of similar diagnoses. Further development is needed for prediction of diagnoses with common symptoms and related diagnoses. The results still prove, however, that unstructured data possesses value beneficial for clinical decision support. If further analyzed with structured data, a more accurate intelligent agent may be explored

Characterizing Instructional Leader Interactions in a Social Learning Management System using Social Network Analysis

Online learning environments are designed with specific users and respective roles in mind. However, for social systems that thrive on the interaction between and among users, important features are developed based on relationships that evolve over time. Typical learning management systems are designed with the teacher and the student as primary users of the system. my.eskwela is a social learning management system that has been designed for use in public schools in the Philippines with the inclusion of an additional user, the school administrator. Although administrators influence on student learning through mediated effects of instructional leadership, pieces of literature are few that provide evidence of their presence in the learning environment, specifically in the implementation of sLMS. By applying Social Network Analysis in the interaction logs from a sLMS that includes instructional leaders in its implementation, this paper aims to answer the question: Can instructional leadership be manifested in social system interactions? Using measures of centrality in social network analysis, results show that administrators play a key role in the network as main drivers of the network information flow. The results affirm the explicit presence of instructional leadership in the implementation of my.eskwela. In addition, sLMS should provide a means for administrator to monitor activities in enforcing mediated learning to students. Contribution of this study is on the the method to verify the instructional leadership of administrators in its inclusion in the implementation of sLMS

The efficacy of an EMR-enabled text messaging system to the expanded health beliefs, diabetes care profile and HbA1c of diabetes mellitus patients

As diabetes mellitus (DM) becomes a global emergency, there is a need to explore novel interventions to address problems in self – management. Literature agree in the potential of mobile phones to carry-out self-care for a wide-array of disease conditions. Diabetes Self – Management Support and Education Through Text – Messaging (DSMSET) is a low-cost, two-way text messaging system designed to deliver self - help, educational messages based on the nine (9) dimensions of health management. DSMSET serves as a plugin to SHINE OS+, an open – source electronic medical record (EMR) system. The research is also based on the Expanded Health Belief Model and explores the efficacy of SMS in improving Expanded Health Beliefs, Diabetes Care Profile and in decreasing HbA1C of adult patients with DM. A two-arm, randomized controlled trial, a total of 122 eligible subjects from UERM PO Domingo OPD Services Department of Medicine and Sweet Diabetics Club based in CHAMP Wellness Clinic were enrolled. Using simple table random digits, subjects were divided equally to trial arms between SMS and non – SMS. The SMS group received DSMSET intervention for 90 days and were required to reply pre-set codes. Both groups answered two sets of survey questionnaires. Patient profile data including demographics for both groups were collected using SHINE OS+ before and after 90-day period. At follow-up, 110 participants were distributed equally and were subjected to analysis with descriptive and inferential statistics using STATA15 and SPSS23. Results show that Total Expanded Health Belief Scores (8 Constructs), Diabetes Care Profile Score, Likelihood to Take Action Score of the SMS group increased compared to the non-SMS group. Combined Expanded Health Belief Scores also showed modest improvement. However, SMS group posted relatively unchanged HbA1c levels while the non-SMS group had increased HbA1c on average

Designing Mobile Educational Games on Voter‟s Education: A Tale of Three Engines

The rapid growth of mobile learning is influenced by the ability to access learning content anytime and anywhere. The on demand capability is available because mobile devices allow for convergence of internet and communications technologies. At the same time, the availability of engines makes development of mobile applications faster and seamless. However, not all mobile development engines are alike. This paper discusses on the development of mobile learning applications using mobile development engines in teaching Filipinos on responsible voting. Specifically, this paper discusses how AndEngine, Ren’Py, and homegrown Usbong were used to develop a mobile board game and a mobile comic book to promote responsible voting to the Filipino youth

Understanding the Behavior of Filipino Twitter Users during Disaster

The Philippines is a country that frequentlyexperiences disasters, such as typhoons. During these events,many citizens spread information and communicate with eachother through social media like Twitter. This study aims to takeadvantage of that fact by analyzing the data from social media toget some insights on the situation. Specifically, this paper studiesthe behavior of Filipinos on Twitter during a disaster, and tries tosee the differences between participants, or the direct victims ofthe disaster, and observers. The study used Latent DirichletAllocation and Principal Component Analysis to extract thedifferent topics discussed during a disaster, and found out whichtopics participants are more likely to talk about. Results also showwhich topics are more likely to be retweeted, which languageparticipants in disaster use more often, and what emotions arepresent in the disaster-time tweets of Filipinos