A Self-Regulated Learning Approach to Educational Recommender Design

Recommender systems, or recommenders, are information filtering systems prevalent today in many fields. One type of recommender found in the field of education, the educational recommender, is a key component of adaptive learning solutions as these systems avoid “one-size-fits-all” approaches by tailoring the learning process to the needs of individual learners. To function, these systems utilize learning analytics in a student-facing manner. While existing research has shown promise and explores a variety of types of educational recommenders, there is currently a lack of research that ties educational theory to the design and implementation of these systems. The theory considered here, self-regulated learning, is underexplored in educational recommender research. Self-regulated learning advocates a cyclical feedback loop that focuses on putting students in control of their learning with consideration for activities such as goal setting, selection of learning strategies, and monitoring of one’s performance. The goal of this research is to explore how best to build a self-regulated learning guided educational recommender and discover its influence on academic success. This research applies a design science methodology in the creation of a novel educational recommender framework with a theoretical base in self-regulated learning. Guided by existing research, it advocates for a hybrid recommender approach consisting of knowledge-based and collaborative filtering, made possible by supporting ontologies that represent the learner, learning objects, and learner actions. This research also incorporates existing Information Systems (IS) theory in the evaluation, drawing further connections between these systems and the field of IS. The self-regulated learning-based recommender framework is evaluated in a higher education environment via a web-based demonstration in several case study instances using mixed-method analysis to determine this approach’s fit and perceived impact on academic success. Results indicate that the self-regulated learning-based approach demonstrated a technology fit that was positively related to student academic performance while student comments illuminated many advantages to this approach, such as its ability to focus and support various studying efforts. In addition to contributing to the field of IS research by delivering an innovative framework and demonstration, this research also results in self-regulated learning-based educational recommender design principles that serve to guide both future researchers and practitioners in IS and education

Channel strategy: Formulation and adaptation

Inspired by open systems theories like the structural contingency theory (Lawrence and Lorsch 1967), population ecology theory (Hannan and Freeman 1977), and resource dependence theory (Pfeffer and Salancik 1978), several marketing scholars have investigated how channels adapt and organize themselves to cope with their environments. Curiously, however, the implication of such adaptive behaviour (i.e., the better adapted firms are more profitable) has not been investigated in the marketing literature. This paper aims to probe that question. Moreover, unlike previous marketing studies, we articulate the manufacturer's rather than the distributor's point-of-view, because channel strategy decisions are usually in the manufacturer's domain. We scrutinize firms' adaptive responses from a channel structure and channel task perspective. Results show that the better adapted firms deliver superior performance, and that the adaptive responses often occur subtly at the specific channel task level even when the channel structure itself may appear seemingly unaltered.structural contingency theory; population ecology theory; resource dependence theory;

Pilot and Feasibility Test of an Implementation Intention Intervention to Improve Fruit and Vegetable Intake Among Women with Low Socioeconomic Status

Fruit and vegetable intake (FVI), a modifiable risk factor for chronic diseases, is lower in low socioeconomic status (SES) populations. Implementation intentions (a specific type of planning that extends the Theory of Planned Behavior) has been studied to improve FVI, but not exclusively with low SES groups. Using mixed methods, we evaluated the feasibility, acceptability, and preliminary efficacy of an implementation intention intervention (versus a general plan) to increase FVI in women with low SES. For the pilot randomized controlled trial, demographics, body mass index, attitude, perceived behavioral control, goal intention strength, and FVI were measured at baseline and FVI again 1-month following the intervention. Feasibility data were collected for recruitment, randomization, retention, and assessment procedures and compared to predetermined targets. Semi-structured interview data was analyzed for emergent themes regarding acceptability of the trial. Preliminary efficacy of the intervention to improve FVI was analyzed descriptively. Feasibility targets were met for randomization (100% vs. ≥80% target), retention (93.5% vs. ≥70% target) and the assessment metrics missing data points (2% vs. ≤10% target) and days from intervention to follow up (mean=69.2, sd=42.6 vs.days). Targets for recruitment were not met with the exception of participants giving informed consent (100% vs. ≥70% target). Participants described the intervention as enjoyable and reported behavioral constructs outside of those measured as important to improve FVI. Limited efficacy analysis suggested that both groups increased their FVI (experimental: +0.17 servings per day, 95% CI: -0.85, 1.20; control: +0.50 servings per day, 95% CI: -0.56, 1.58). Further research which examines interventions based upon behavior change models to improve dietary health behaviors in marginalized groups is needed

Pilot and feasibility test of an implementation intention intervention to improve fruit and vegetable intake among women with low socioeconomic status

Fruit and vegetable intake (FVI), a modifiable risk factor for chronic diseases, is lower in low socioeconomic status (SES) populations. Implementation intentions (a specific type of planning that extends the Theory of Planned Behavior) has been studied to improve FVI, but not exclusively with low SES groups. Using mixed methods, we evaluated the feasibility, acceptability, and preliminary efficacy of an implementation intention intervention (versus a general plan) to increase FVI in women with low SES. For the pilot randomized controlled trial, demographics, body mass index, attitude, perceived behavioral control, goal intention strength, and FVI were measured at baseline and FVI again 1-month following the intervention. Feasibility data were collected for recruitment, randomization, retention, and assessment procedures and compared to predetermined targets. Semi-structured interview data was analyzed for emergent themes regarding acceptability of the trial. Preliminary efficacy of the intervention to improve FVI was analyzed descriptively. Feasibility targets were met for randomization (100% vs. ≥80% target), retention (93.5% vs. ≥70% target) and the assessment metrics missing data points (2% vs. ≤10% target) and days from intervention to follow up (mean=69.2, sd=42.6 vs.days). Targets for recruitment were not met with the exception of participants giving informed consent (100% vs. ≥70% target). Participants described the intervention as enjoyable and reported behavioral constructs outside of those measured as important to improve FVI. Limited efficacy analysis suggested that both groups increased their FVI (experimental: +0.17 servings per day, 95% CI: -0.85, 1.20; control: +0.50 servings per day, 95% CI: -0.56, 1.58). Further research which examines interventions based upon behavior change models to improve dietary health behaviors in marginalized groups is needed

Italian Consensus Statement on Patient Engagement in Chronic Care: Process and Outcomes

Patient engagement has been recognized as a key priority in chronic care. However,scholars agree that guidelines are needed to ensure effective patient engagement strategies. Tothis end, a Consensus Conference process was promoted with the following methodological steps:(1) extensive literature review about patient engagement initiatives in chronic care; (2) a stakeholderssurvey to collect best practices and (3) workshops with experts. On the basis of the informationcollected, a consensus statement was drafted, revised, and finalized by a panel of select renownedexperts. These experts agreed in defining engagement as an eco-systemic concept involving multipleactors all of which contribute to influence patients\u2019 willingness and ability to engage in chronic care.Moreover, experts recommended, whenever possible, to adopt standardized instruments to assess engagement levels and related unmet needs. Then, experts strongly advised appropriate trainings for healthcare professionals about patient engagement strategies. Furthermore, the importance of promoting healthcare professionals\u2019 wellbeing has been advocated. Family caregivers, as well as patients\u2019 organizations - should be trained and engaged to increase the effectiveness of interventions dedicated to patients. Finally, experts agreed that digital technologies should be considered as acrucial enhancer for patient engagement in chronic car

Feasibility of Sleep Self-Management Intervention in Pregnancy Using a Personalized Health Monitoring Device: Protocol for a Pilot Randomized Controlled Trial

Background: Sleep disruptions are common during pregnancy and associated with increased risk of adverse maternal outcomes such as preeclampsia, gestational diabetes, prolonged labor, and cesarean birth. Given the morbidity associated with poor sleep, cost-effective approaches to improving sleep that can be disseminated in community or clinical settings are needed. Personal health monitor (PHM) devices offer an opportunity to promote behavior change, but their acceptability and efficacy at improving sleep in pregnant women are unknown. Objective: The goal of the paper is to describe the protocol for an ongoing pilot randomized controlled trial that aims to establish the feasibility, acceptability, and preliminary efficacy of using a PHM device (Shine 2, Misfit) to promote sleep during pregnancy. Methods: The proposed pilot study is a 12-week, parallel arm, randomized controlled trial. Pregnant women, at 24 weeks gestation, will be randomized at a 1:1 ratio to a 12-week sleep education plus PHM device group or a sleep education alone comparison group. The primary outcomes will be measures of feasibility (ie, recruitment, enrollment, adherence) and acceptability (ie, participant satisfaction). The secondary outcomes will be self-reported sleep quality and duration, excessive daytime sleepiness, fatigue, and depressive symptoms. Results: Recruitment for this study began in September 2017 and ended in March 2018. Data collection for the primary and secondary aims was completed in August 2018. We anticipate that the data analysis for primary and secondary aims will be completed by December 2019. The results from this trial will inform the development of a larger National Institutes of Health grant application to test the efficacy of an enhanced version of the sleep intervention that we plan to submit in the year 2020. Conclusions: This study will be the first to apply a PHM device as a tool for promoting self-management of sleep among pregnant women. PHM devices have the potential to facilitate behavioral interventions because they include theory-driven, self-regulatory techniques such as behavioral self-monitoring. The results of the study will inform the development of a sleep health intervention for pregnant women. Trial Registration: ClinicalTrials.gov NCT03783663; https://clinicaltrials.gov/ct2/show/NCT03783663 (Archived by WebCite at http://www.webcitation.org/779Ou8hon) International Registered Report Identifier (IRRID): DERR1-10.2196/1245

Applying adaptive learning by integrating semantic and machine learning in proposing student assessment model

Adaptive learning is one of the most widely used data driven approach to teaching and it received an increasing attention over the last decade. It aims to meet the student’s characteristics by tailoring learning courses materials and assessment methods. In order to determine the student’s characteristics, we need to detect their learning styles according to visual, auditory or kinaesthetic (VAK) learning style. In this research, an integrated model that utilizes both semantic and machine learning clustering methods is developed in order to cluster students to detect their learning styles and recommend suitable assessment method(s) accordingly. In order to measure the effectiveness of the proposed model, a set of experiments were conducted on real dataset (Open University Learning Analytics Dataset). Experiments showed that the proposed model is able to cluster students according to their different learning activities with an accuracy that exceeds 95% and predict their relative assessment method(s) with an average accuracy equals to 93%

A Novel Adaptation Model for E-Learning Recommender Systems Based on Student’s Learning Style

In recent years, a substantial increase has been witnessed in the use of online learning resources by learn- ers. However, owing to an information overload, many find it difficult to retrieve appropriate learning resources for meeting learning requirements. Most of the existing systems for e-learning make use of a “one-size-fits-all” approach, thus providing all learners with the same content. Whilst recommender systems have scored notable success in the e-commerce domain, they still suffer from drawbacks in terms of making the right recommendations for learning resources. This can be attributed to the differences among learners’ preferences such as varying learning styles, knowledge levels and sequential learning patterns. Hence, to identify the needs of an individual student, e-learning systems that can build profiles of student preferences are required. In addition, changing students’ preferences and multidimensional attributes of the course content are not fully considered simultaneously. It is by failing to review these issues that existing recommendation algorithms often give inaccurate recommendations. This thesis focuses on student learning styles, with the aim of dynamically tailoring the learning process and course content to meet individual needs. The proposed Ubiquitous LEARNing (ULEARN) system is an adaptive e-learning recommender system geared towards providing a personalised learning environ- ment, which ensures that course learning objects are in line with the learner’s adaptive profile. This thesis delivers four main contributions: First, an innovative algorithm which dynamically reduces the number of questions in the Felder-Silverman Learning Styles (FSLSM) questionnaire for the purpose of initialising student profiles has been proposed. The second contribution comprises examining the accuracy of various similarity metrics so as to select the most suitable similarity measurements for learning objects recommendation algorithm. The third contribution includes an Enhanced Collaboration Filtering (ECF) algorithm and an Enhanced Content-Based Filtering (ECBF) algorithm, which solves the issues of cold-start and data sparsity in- herent to the traditional Collaborative Filtering (CF) and the traditional Content-based Filtering (CBF), respectively. Moreover, these two new algorithms have been combined to create a new Enhanced Hybrid Filtering (EHF) algorithm that recommends highly accurate personalised learning objects on the basis of the stu- dents’ learning styles. The fourth contribution is a new algorithm that tracks patterns of student learning behaviours and dynam- ically adapts the student learning style accordingly. The ULEARN recommendation system was implemented with Visual Studio in C++ and Windows Pre- sentation Foundation (WPF) for the development of the Graphical User Interface (GUI). The experimental results revealed that the proposed algorithms have achieved significant improvements in student’s profile adaptation and learning objects recommendation in contrast with strong benchmark models. Further find- ings from experiments indicated that ULEARN can provide relevant learning object recommendations based on students’ learning styles with the overall students’ satisfaction at almost 90%. Furthermore, the results showed that the proposed system is capable of mitigating the problems data sparsity and cold-start, thereby improving the accuracy and reliability of recommendation of the learning object. All in all, the ULEARN system is competent enough to support educational institutions in recommending personalised course content, improving students’ performance as well as promoting student engagement.Arab academy for science technology & maritime transpor