Purpose – The purpose of this paper is to assess the research efficiency of the Chilean higher education institutions (HEIs). As it has been argued in the literature, universities in Chile are far from being considered research-oriented institutions. Current governmental reforms have put pressures on the efficient use of public resources, especially, public expenditures in higher education. In response, the proposed data-driven approach can be used to inform educational managers and policy makers about research efficiency. Therefore, a better allocation of the scarce educational resources can be achieved. Design/methodology/approach – Data envelopment analysis is used to assess the research efficiency of a set of Chilean universities. Four models are proposed based on different parameters to cover various drivers of the research productivity. Findings – The paper provides evidence that only a few universities in Chile are efficient in regards to research. Moreover, interesting results in terms of the differences in efficiency between traditional universities and private universities were found. Universities with a mixed-funding structure (private traditional) are more efficient than both public and purely private universities. Additionally, universities that receive direct funds from the government are on average 3.3 times more efficient than private universities. According to the models, only one private university appeared at the top 10 based on the research efficiency ranking. Practical implications – Current pressures in the funding structures of the higher education system have led to an increased awareness in the utilization of resources. The results provided in this study are useful for guiding a better allocation of public resources and providing insights about efficient funding structures. Originality/value – An understanding of the current status of research efficiency and the identification of the best performers allows educational managers to improve their resource allocation processes.

Purpose This article quantifies complexity in translational research. The impact of major operational steps and technical requirements (TR) is calculated with respect to their ability to accelerate moving new discoveries into clinical practice. Design/Methodology/Approach A three-phase integrated Quality Function Deployment (QFD) and Analytic Hierarchy Process (AHP) method was used to quantify complexity in translational research. A case study in obesity was used to usability. Findings Generally, the evidence generated was valuable for understanding various components in translational research. Particularly, we found that collaboration networks, multidisciplinary team capacity and community engagement are crucial for translating new discoveries into practice. Research limitations/implications As the method is mainly based on subjective opinion, some argue that the results may be biased. However, a consistency ratio is calculated and used as a guide to subjectivity. Alternatively, a larger sample may be incorporated to reduce bias. Practical implications The integrated QFD-AHP framework provides evidence that could be helpful to generate agreement, develop guidelines, allocate resources wisely, identify benchmarks and enhance collaboration among similar projects. Originality/value Current conceptual models in translational research provide little or no clue to assess complexity. The proposed method aimed to fill this gap. Additionally, the literature review includes various features that have not been explored in translational research.

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In the United States, the greatest decline in the number of students in the STEM education pipeline occurs at the university level, where students, who were initially interested in STEM fields, drop-out or move on to other interests. It has been reported that “of the 23 most commonly cited reasons for switching out of STEM, all but 7 had something to do with the pedagogical experience.” Thus, understanding the characteristics of the pedagogical experience that impact students' interest in STEM is of great importance to the academic community. This work tests the hypothesis that there exists a correlation between the semantic structure of lecture content and students' affective states. Knowledge gained from testing this hypothesis will inform educators of the specific semantic structure of lecture content that enhance students' affective states and interest in course content, toward the goal of increasing STEM retention rates and overall positive experiences in STEM majors. A case study involving a series of science and engineering based digital content is used to create a semantic network and demonstrate the implications of the methodology. The results reveal that affective states such as engagement and boredom are consistently strongly correlated to the semantic network metrics outlined in the paper, while the affective state of confusion is weakly correlated with the same semantic network metrics. The results reveal semantic network relationships that are generalizable across the different textually derived information sources explored. These semantic network relationships can be explored by researchers trying to optimize their message structure in order to have its intended effect.