Sequence of radiotherapy and chemotherapy in breast cancer after breast-conserving surgery

Purpose The optimal sequence of radiotherapy and chemotherapy in breast-conserving therapy is unknown. Methods and Materials From 1983 through 2007, a total of 641 patients with 653 instances of breast-conserving therapy (BCT), received both chemotherapy and radiotherapy and are the basis of this analysis. Patients were divided into three groups. Groups A and B comprised patients treated before 2005, Group A radiotherapy first and Group B chemotherapy first. Group C consisted of patients treated from 2005 onward, when we had a fixed sequence of radiotherapy first, followed by chemotherapy. Results Local control did not show any differences among the three groups. For distant metastasis, no difference was shown between Groups A and B. Group C, when compared with Group A, showed, on univariate and multivariate analyses, a significantly better distant metastasis–free survival. The same was noted for disease-free survival. With respect to disease-specific survival, no differences were shown on multivariate analysis among the three groups. Conclusion Radiotherapy, as an integral part of the primary treatment of BCT, should be administered first, followed by adjuvant chemotherapy

VAST: a practical validation framework for e-assessment solutions

The influx of technology in education has made it increasingly difficult to assess the validity of educational assessments. The field of information systems often ignores the social dimension during validation, whereas educational research neglects the technical dimensions of designed instruments. The inseparability of social and technical elements forms the bedrock of socio-technical systems. Therefore, the current lack of validation approaches that address both dimensions is a significant gap. We address this gap by introducing VAST: a validation framework for e-assessment solutions. Examples of such solutions are technology-enhanced learning systems and e-health applications. Using multi-grounded action research as our methodology, we investigate how we can synthesise existing knowledge from information systems and educational measurement to construct our validation framework. We develop an extensive user guideline complementing our framework and find through expert interviews that VAST facilitates a comprehensive, practical approach to validating e-assessment solutions.Horizon 2020(H2020)883588Algorithms and the Foundations of Software technolog

Embracing trustworthiness and authenticity in the validation of learning analytics systems

Learning analytics sits in the middle space between learning theory and data analytics. The inherent diversity of learning analytics manifests itself in an epistemology that strikes a balance between positivism and interpretivism, and knowledge that is sourced from theory and practice. In this paper, we argue that validation approaches for learning analytics systems should be cognisant of these diverse foundations. Through a systematic review of learning analytics validation research, we find that there is currently an over-reliance on positivistic validity criteria. Researchers tend to ignore interpretivistic criteria such as trustworthiness and authenticity. In the 38 papers we analysed, researchers covered positivistic validity criteria 221 times, whereas interpretivistic criteria were mentioned 37 times. We motivate that learning analytics can only move forward with holistic validation strategies that incorporate “thick descriptions” of educational experiences. We conclude by outlining a planned validation study using argument-based validation, which we believe will yield meaningful insights by considering a diverse spectrum of validity criteria.Horizon 2020(H2020)883588Algorithms and the Foundations of Software technolog

Aligning the goals of learning analytics with its research scholarship: an open peer commentary approach

To promote cross-community dialogue on matters of significance within the field of learning analytics (LA), we as editors-in-chief of the Journal of Learning Analytics (JLA) have introduced a section for papers that are open to peer commentary. An invitation to submit proposals for commentaries on the paper was released, and 12 of these proposals were accepted. The 26 authors of the accepted commentaries are based in Europe, North America, and Australia. They range in experience from PhD students and early-career researchers to some of the longest-standing, most senior members of the learning analytics community. This paper brings those commentaries together, and we recommend reading it as a companion piece to the original paper by Motz et al. (2023), which also appears in this issue.Horizon 2020(H2020)883588Algorithms and the Foundations of Software technolog