Adaptive User-Controlled Personalization for Virtual Journey Applications
Bibliographic record
Abstract
Recent developments in information systems, computer hardware, and software industries have made digital transformation more straightforward. Investments in digital transformation technologies and services worldwide are significantly increasing, and the projected investment is forecasted to reach 2.8 trillion USD by 2025, with this amount being more than the world's sixth economy by gross domestic product. Against the backdrop of these ongoing digital transitions, handheld devices have become an extremely important form of digital technology. A virtual journey is a type of application common on eCommerce domains that is becoming an integral part of higher education information communication platforms. Higher education institutions (HEIs) incorporate digital and non-digital decentralized services that are integrated and centralized under a single application or piece of software. Generally, a university website is the standard result. Just like in businesses, HEIs provide their information via websites for their campus population and other users outside the institution. However, websites are not capable of delivering personalized user experiences to their users. Virtual journey applications (VJAs) have the foundation to smoothly integrate and centralize all available digital and non-digital services and decentralized services under a single application or piece of software. Virtual campus journey applications (VCJAs) serve a wide variety of users, such as students, prospective students, doctoral students, parents, university visitors, academic staff, and administrative and other staff to gain access to education services through handheld devices. Users of digital services have their own respective preferences. Delivering all services within a single application makes it not very useful due to the overload of information. To deliver a personalized user experience based on individual users’ needs, this dissertation looks into the following objectives. This dissertation investigates the general characteristics of VCJAs by benchmarking the top 100 universities in the world and evaluating them against their users (e.g., university students, faculty, and staff) to identify user satisfaction levels. It aims to determine the characteristics of VJAs in general and identify the main shortcomings of information communication and dissimilation in digital services. It also introduces user-controlled personalization into a virtual journey to overcome information overload in VJAs. Finally, it aims to provide an approach to enhance the usefulness of information communication and dissemination for virtual journey services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".