Marketing methods for electronic resources in medical libraries: a study on the application of the analytical hierarchy process

Objective:Paired with the high cost of providing access to electronic resources in medical libraries, the inefficient use of these resources highlights the need for more efforts to promote these resources than ever before. In this study, electronic resource marketing methods were prioritized and the best strategies were determined using the analytical hierarchy process (AHP).Methods:Using an analytical survey of officials of medical libraries, the most common methods for marketing electronic resources in libraries were determined and divided into categories of strategies. Five important criteria for marketing strategies were also selected. Using the analytical hierarchy process, pairwise comparisons were performed between the alternatives (i.e., strategies), which were evaluated against the selected criteria. Data analysis was performed using Expert Choice 11 software.Results:A total of 44 electronic resource marketing methods were identified and categorized into 4 strategies. On average, 43.9% of these methods were used by the surveyed libraries. The analytical hierarchy process showed that simplicity was the most important criterion and that communication networks were the best electronic resource marketing strategy. Home/off-campus access, group training, library search stations, and marketing by individual librarians were the most preferred methods of marketing electronic resources.Conclusion:With the availability of a variety of different methods for marketing electronic resources, medical libraries must select strategies based on important criteria depending on the characteristics of the library, librarians, and users. Thus, the analytical hierarchy process can be an effective and practical solution to decision-making by mathematically prioritizing the selection of the best strategies from a set of alternatives based on differentially weighted criteria.     

PERSON: Personalized information retrieval evaluation based on citation networks

Despite the importance of personalization in information retrieval, there is a big lack of standard datasets and methodologies for evaluating personalized information retrieval (PIR) systems, due to the costly process of producing such datasets. Subsequently, a group of evaluation frameworks (EFs) have been proposed that use surrogates of the PIR evaluation problem, instead of addressing it directly, to make PIR evaluation more feasible. We call this group of EFs, indirect evaluation frameworks. Indirect frameworks are designed to be more flexible than the classic (direct) ones and much cheaper to be employed. However, since there are many different settings and methods for PIR, e.g., social-network-based vs. profile-based PIR, and each needs some special kind of data to do the personalization based on, not all the evaluation frameworks are applicable to all the PIR methods. In this paper, we first review and categorize the frameworks that have already been introduced for evaluating PIR. We further propose a novel indirect EF based on citation networks (called PERSON), which allows repeatable, large-scale, and low-cost PIR experiments. It is also more information-rich compared to the existing EFs and can be employed in many different scenarios. The fundamental idea behind PERSON is that in each document (paper) d, the cited documents are generally related to d from the perspective of d’s author(s). To investigate the effectiveness of the proposed EF, we use a large collection of scientific papers. We conduct several sets of experiments and demonstrate that PERSON is a reliable and valid EF. In the experiments, we show that PERSON is consistent with the traditional Cranfield-based evaluation in comparing non-personalized IR methods. In addition, we show that PERSON can correctly capture the improvements made by personalization. We also demonstrate that its results are highly correlated with those of another salient EF. Our experiments on some issues about the validity of PERSON also show its validity. It is also shown that PERSON is robust w.r.t. its parameter settings.     

21st Century experiences in the development of school-based management policy and practices in Indonesia

Educational Research for Policy and Practice - Since 2001, Indonesian schools have implemented a mandatory school-based management (SBM) policy for better quality education in general and more...     

Understanding privacy and data protection issues in learning analytics using a systematic review

The field of learning analytics has advanced from infancy stages into a more practical domain, where tangible solutions are being implemented. Nevertheless, the field has encountered numerous privacy and data protection issues that have garnered significant and growing attention. In this systematic review, four databases were searched concerning privacy and data protection issues of learning analytics. A final corpus of 47 papers published in top educational technology journals was selected after running an eligibility check. An analysis of the final corpus was carried out to answer the following three research questions: (1) What are the privacy and data protection issues in learning analytics? (2) What are the similarities and differences between the views of stakeholders from different backgrounds on privacy and data protection issues in learning analytics? (3) How have previous approaches attempted to address privacy and data protection issues? The results of the systematic review show that there are eight distinct, intertwined privacy and data protection issues that cut across the learning analytics cycle. There are both cross-regional similarities and three sets of differences in stakeholder perceptions towards privacy and data protection in learning analytics. With regard to previous attempts to approach privacy and data protection issues in learning analytics, there is a notable dearth of applied evidence, which impedes the assessment of their effectiveness. The findings of our paper suggest that privacy and data protection issues should not be relaxed at any point in the implementation of learning analytics, as these issues persist throughout the learning analytics development cycle. One key implication of this review suggests that solutions to privacy and data protection issues in learning analytics should be more evidence-based, thereby increasing the trustworthiness of learning analytics and its usefulness.

Practitioner notes

What is already known about this topic

• Research on privacy and data protection in learning analytics has become a recognised challenge that hinders the further expansion of learning analytics.
• Proposals to counter the privacy and data protection issues in learning analytics are blurry; there is a lack of a summary of previously proposed solutions.

What this study contributes

• Establishment of what privacy and data protection issues exist at different phases of the learning analytics cycle.
• Identification of how different stakeholders view privacy, similarities and differences, and what factors influence their views.
• Evaluation and comparison of previously proposed solutions that attempt to address privacy and data protection in learning analytics.

Implications for practice and/or policy

• Privacy and data protection issues need to be viewed in the context of the entire cycle of learning analytics.
• Stakeholder views on privacy and data protection in learning analytics have commonalities across contexts and differences that can arise within the same context. Before implementing learning analytics, targeted research should be conducted with stakeholders.
• Solutions that attempt to address privacy and data protection issues in learning analytics should be put into practice as far as possible to better test their usefulness.