HELPeR: Interface Design Decision and Evaluation.

Inequities in health information access contribute to disparities in health outcomes. Health recommender systems have emerged as a promising solution to help users find the right information. Despite their various applications, it remains understudied how these systems can aid cancer patients. In this paper, we introduce HELPeR, a recommender system designed to assist ovarian cancer patients with their information needs. The design addresses cold-start challenges, drawing input from health experts and ovarian cancer forum posts. We evaluated HELPeR with nurse practitioners in a cold-start scenario, highlighting its benefits and areas for future improvement.

Utilizing Digital Twin to Create Personas Representing Ovarian Cancer Patients and Their Families.

OvCa patients and caregivers perceived challenges in online health information seeking. The HELPeR recommendation system utilized digital twins to create personas reflecting real-world OvCa patients and caregivers. The aim of this study was to describe the creation of digital twins and demonstrate their use cases in the study. Digital twins of OvCa patients and caregivers were created by triangulating multiple sources, including online cancer forums, direct interviews with patients and caregivers, domain expert input, and clinical notes. 10 personas were created for both OvCa patients and caregivers who had a variety of cancer trajectories and information interests. These digital twins present a potential solution for training artificial intelligence models at the initial phase when there is a scarcity of user information.

Towards Building an E-Librarian System: Exploring Recommendation System Preferences Among Ovarian Cancer Patients and Caregivers.

Ovarian cancer (OvCa) patients encounter complex treatment decisions, and often have difficulties in searching and integrating online health information to guide their treatment decision-making. The objective of this study was to explore the preference of online health information among OvCa patients and caregivers, by exploring their preferred content, format, and function features for the design of a personalized recommender system. This study used qualitative research methods to collect data through in-depth interviews with 18 OvCa patients and 2 caregivers. A total of (N=20) face-to-face interviews were conducted, and subsequently analyzed by audio recordings, verbatim transcription, and theory-driven approach with thematic analysis. A total of 5 themes were identified for content-related design, 4 themes identified for system function and one theme identified for frequency format. The results of this study inform the preference and therefore OvCa specific features can be tailor-made in a recommendation system.

Women with ovarian cancer's information seeking and avoidance behaviors: an interview study.

Objectives: Despite the importance of using information for ovarian cancer (OvCa) disease management and decision-making, some women with OvCa do not actively seek out information. The purpose of this study is to investigate factors that influence information seeking behaviors and information avoidance behaviors and information resources among women with OvCa and their caregivers. Materials and methods: We conducted in-depth interviews with OvCa patients or caregivers of OvCa (n = 20) and employed deductive and inductive coding methodologies for analysis. Results: Our analysis revealed 5 emerging themes associated with active information seeking behavior, 5 themes of passive information acquisition, and 4 themes of information avoidance behavior. Additionally, we identified participants' preferred information sources for OvCa management, such as health organization or government operated resources and web-based social groups. Discussion: To enhance information access, strategies should be developed to motivate people with OvCa to seek rather than avoid information. The study emphasizes the significance of promoting patient-provider communication and leveraging strong social support networks for effective information acquisition. Conclusion: Our findings provide valuable implications for clinical practice and policymaking, emphasizing the need to improve access to information for individuals with OvCa. By addressing the identified factors influencing information seeking behaviors, healthcare professionals and policymakers can better support patients and caregivers in their information-seeking journey, ultimately enhancing disease management and decision-making outcomes.

Knowledge Acquisition and Social Support in Online Health Communities: Analysis of an Online Ovarian Cancer Community.

BACKGROUND: Patients and caregivers widely use online health communities (OHCs) to acquire knowledge from peers. Questions posed in OHCs reflect participants' learning objectives and differ in their level of cognitive complexity. However, little is known about the topics and levels of participants' learning objectives and the corresponding support they receive from members of OHCs. OBJECTIVE: This study aimed to investigate the knowledge acquisition of patients and caregivers in an OHC. Specifically, we investigated the distribution and topics of posts with learning objectives at different cognitive complexity levels, the type and amount of social support provided to meet users' learning objectives at different cognitive complexity levels, and the influence of social support on the change in learning objectives. METHODS: We collected 10 years of discussion threads from one of the most active ovarian cancer (OvCa) OHCs. A mixed methods approach was used, including qualitative content analysis and quantitative statistical analysis. Initial posts with questions were manually classified into 1 of the 3 learning objectives with increasing cognitive complexity levels, from low to high, based on the Anderson and Krathwohl taxonomy: understand, analyze, and evaluate. Manual content analysis and automatic classification models were used to identify the types of social support in the comments, including emotional support and 5 types of informational support: advice, referral, act, personal experience, and opinion. RESULTS: The original data set contained 909 initial posts and 14,816 comments, and the final data set for the analysis contained 560 posts with questions and 3998 comments. Our results showed that patients with OvCa and their caregivers mainly used OHCs to acquire knowledge for low- to medium-level learning objectives. Of the questions, 82.3% (461/560) were either understand- or analyze-level questions, in which users were seeking to learn basic facts and medical concepts or draw connections among different situations and conditions. Only 17.7% (99/560) of the questions were at the evaluate level, in which users asked other OHC members to help them make decisions or judgments. Notably, OvCa treatment was the most popular topic of interest among all the questions, regardless of the level of learning objectives. Regarding the social support received for different levels of learning objectives, significant differences were found in the advice (F2437.84=9.69; P<.001), opinion (F2418.18=11.56; P<.001), and emotional support (F2395.88=3.24; P=.01), as determined by one-way ANOVA, whereby questions at the evaluate level were more likely to receive advice, opinion, and emotional support than questions at the lower levels. Additionally, receiving social support tends to drive users to increase the cognitive complexity of the learning objective in the next post. CONCLUSIONS: Our study establishes that OHCs are promising resources for acquiring knowledge of OvCa. Our findings have implications for designing better OHCs that serve the growing OvCa community.

Exploring Behavioral Patterns for Data-Driven Modeling of Learners' Individual Differences.

Educational data mining research has demonstrated that the large volume of learning data collected by modern e-learning systems could be used to recognize student behavior patterns and group students into cohorts with similar behavior. However, few attempts have been done to connect and compare behavioral patterns with known dimensions of individual differences. To what extent learner behavior is defined by known individual differences? Which of them could be a better predictor of learner engagement and performance? Could we use behavior patterns to build a data-driven model of individual differences that could be more useful for predicting critical outcomes of the learning process than traditional models? Our paper attempts to answer these questions using a large volume of learner data collected in an online practice system. We apply a sequential pattern mining approach to build individual models of learner practice behavior and reveal latent student subgroups that exhibit considerably different practice behavior. Using these models we explored the connections between learner behavior and both, the incoming and outgoing parameters of the learning process. Among incoming parameters we examined traditionally collected individual differences such as self-esteem, gender, and knowledge monitoring skills. We also attempted to bridge the gap between cluster-based behavior pattern models and traditional scale-based models of individual differences by quantifying learner behavior on a latent data-driven scale. Our research shows that this data-driven model of individual differences performs significantly better than traditional models of individual differences in predicting important parameters of the learning process, such as performance and engagement.

Exploring Resource-Sharing Behaviors for Finding Relevant Health Resources: Analysis of an Online Ovarian Cancer Community.

BACKGROUND: Online health communities (OHCs) provide patients and survivors of ovarian cancer (OvCa) and their caregivers with help beyond traditional support channels, such as health care providers and clinicians. OvCa OHCs promote connections and exchanges of information among users with similar experiences. Users often exchange information, which leads to the sharing of resources in the form of web links. Although OHCs are important platforms for health management, concerns exist regarding the quality and relevance of shared resources. Previous studies have examined different aspects of resource-sharing behaviors, such as the purpose of sharing, the type of shared resources, and peer user reactions to shared resources in OHCs to evaluate resource exchange scenarios. However, there is a paucity of research examining whether resource-sharing behaviors can ultimately determine the relevance of shared resources. OBJECTIVE: This study aimed to examine the association between OHC resource-sharing behaviors and the relevance of shared resources. We analyzed three aspects of resource-sharing behaviors: types of shared resources, purposes of sharing resources, and OHC users' reactions to shared resources. METHODS: Using a retrospective design, data were extracted from the National Ovarian Cancer Coalition discussion forum. The relevance of a resource was classified into three levels: relevant, partially relevant, and not relevant. Resource-sharing behaviors were identified through manual content analysis. A significance test was performed to determine the association between resource relevance and resource-sharing behaviors. RESULTS: Approximately 48.3% (85/176) of the shared resources were identified as relevant, 29.5% (52/176) as partially relevant, and 22.2% (39/176) as irrelevant. The study established a significant association between the types of shared resources (χ218=33.2; P<.001) and resource relevance (through chi-square tests of independence). Among the types of shared resources, health consumer materials such as health news (P<.001) and health organizations (P=.02) exhibited significantly more relevant resources. Patient educational materials (P<.001) and patient-generated resources (P=.01) were more significantly associated with partially relevant and irrelevant resources, respectively. Expert health materials, including academic literature, were only shared a few times but had significantly (P<.001) more relevant resources. A significant association (χ210=22.9; P<.001) was also established between the purpose of resource sharing and overall resource relevance. Resources shared with the purpose of providing additional readings (P=.01) and pointing to resources (P=.03) had significantly more relevant resources, whereas subjects for discussion and staying connected did not include any relevant shared resources. CONCLUSIONS: The associations found between resource-sharing behaviors and the relevance of these resources can help in collecting relevant resources, along with the corresponding information needs from OvCa OHCs, on a large scale through automation. The results from this study can be leveraged to prioritize the resources required by survivors of OvCa and their caregivers, as well as to automate the search for relevant shared resources in OvCa OHCs.

An integrated practice system for learning programming in Python: design and evaluation.

Over the past decades, computer science educators have developed a multitude of interactive learning resources to support learning in various computer science domains, especially in introductory programming. While such smart content items are known to be beneficial, they are frequently offered through different login-based systems, each with its own student identification for giving credits and collecting log data. As a consequence, using more than one kind of smart learning content is rarely possible, due to overhead for both teachers and students caused by adopting and using several systems in the context of a single course. In this paper, we present a general purpose architecture for integrating multiple kinds of smart content into a single system. As a proof of this approach, we have developed the Python Grids practice system for learning Python, which integrates four kinds of smart content running on different servers across two continents. The system has been used over a whole semester in a large-scale introductory programming course to provide voluntary practice content for over 600 students. In turn, the ability to offer four kinds of content within a single system enabled us to examine the impact of using a variety of smart learning content on students' studying behavior and learning outcomes. The results show that the majority of students who used the system were engaged with all four types of content, instead of only engaging with one or two types. Moreover, accessing multiple types of content correlated with higher course performance, as compared to using only one type of content. In addition, weekly practice with the system during the course also correlated with better overall course performance, rather than using it mainly for preparing for the course final examination. We also explored students' motivational profiles and found that students using the system had higher levels of motivation than those who did not use the system. We discuss the implications of these findings.