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Educational data mining and learning analytics promise better understanding of student behavior and knowledge, as well as new information on the tacit factors that contribute to student actions. This knowledge can be used to inform decisions related to course and tool design and pedagogy, and to further engage students and guide those at risk of failure. This working group report provides an overview of the body of knowledge regarding the use of educational data mining and learning analytics focused on the teaching and learning of programming. In a literature survey on mining students' programming processes for 2005-2015, we observe a significant increase in work related to the field. However, the majority of the studies focus on simplistic metric analysis and are conducted within a single institution and a single * Working group leaders Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

As computing becomes a mainstream discipline embedded in the school curriculum and acts as an enabler for an increasing range of academic disciplines in higher education, the literature on introductory programming is growing. Although there have been several reviews that focus on specific aspects of introductory programming, there has been no broad overview of the literature exploring recent trends across the breadth of introductory programming.This paper is the report of an ITiCSE working group that conducted a systematic review in order to gain an overview of the introductory programming literature. Partitioning the literature into papers addressing the student, teaching, the curriculum, and assessment, we explore trends, highlight advances in knowledge over the past 15 years, and indicate possible directions for future research.

We estimate the global BOLD Systems database holds core DNA barcodes (rbcL + matK) for about 15% of land plant species and that comprehensive species coverage is still many decades away. Interim performance of the resource is compromised by variable sequence overlap and modest information content within each barcode. Our model predicts that the proportion of species-unique barcodes reduces as the database grows and that ‘false’ species-unique barcodes remain >5% until the database is almost complete. We conclude the current rbcL + matK barcode is unfit for purpose. Genome skimming and supplementary barcodes could improve diagnostic power but would slow new barcode acquisition. We therefore present two novel Next Generation Sequencing protocols (with freeware) capable of accurate, massively parallel de novo assembly of high quality DNA barcodes of >1400 bp. We explore how these capabilities could enhance species diagnosis in the coming decades.

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