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The ability to predict student performance in a course or program creates opportunities to improve educational outcomes. With effective performance prediction approaches, instructors can allocate resources and instruction more accurately. Research in this area seeks to identify features that can be used to make predictions, to identify algorithms that can improve predictions, and to quantify aspects of student performance. Moreover, research in predicting student performance seeks to determine interrelated features and to identify the underlying reasons why certain features work better than others. This working group report presents a systematic literature review of work in the area of predicting student performance. Our analysis shows a clearly increasing amount of research in this area, as well as an increasing variety of techniques used. At the same time, the review uncovered a number of issues with research quality that drives a need for the community to provide more detailed reporting of methods and results and to increase efforts to validate and replicate work.

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).

Computer science educators are increasingly using interactive learning content to enrich and enhance the pedagogy of their courses.

Achievement badges are increasingly used to enhance educational systems and they have been shown to affect student behavior in different ways. However, details on best practices and effective concepts to implement badges from a non-technical point of view are scarce. We implemented badges to our learning management system, used them on a large course and collected feedback from students. Based on our experiences, we present recommendations to other educators that plan on using badges.

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Executive SummaryParson's programming puzzles are a family of code construction assignments where lines of code are given, and the task is to form the solution by sorting and possibly selecting the correct code lines. We introduce a novel family of Parson's puzzles where the lines of code need to be sorted in two dimensions. The vertical dimension is used to order the lines, whereas the horizontal dimension is used to change control flow and code blocks based on indentation as in Python. Python blocks have no explicit begin/end statements or curly braces to mark where the block starts or stops. Instead, indentation is used to define starts and stops of blocks and functions.In addition, we introduce tools supporting two-dimensional Parson's puzzles: (1) MIT licensed JavaScript widget to embed our puzzles to any HTML, and (2) server to create, share, and solve puzzles.We have observed how experienced programmers solve our puzzles. Such users often start by dragging the method signature to the beginning and continue by defining majority of the control flow (i.e., loop statements, assignments, conditional statements). Only after these are done, details, including initialization of variables and handling of corner cases, are dragged to correct positions in the middle of the previously structured code. This shows that even experts are not able to solve puzzles linearly, i.e., line by line, starting from the first. Thus, user interfaces (UIs) should minimize the work needed to insert a line between two adjacent lines of existing code. In some of the existing Parson's Puzzle UIs this is not the case.Another observation we made is that too often users don't ask or use automated feedback. Why this happens needs further investigations. Perhaps experienced users are too proud to ask a tool to help them (especially when being observed), or perhaps users don't recognize when they are stuck and should ask for help. Providing constant feedback is one way to tackle this problem. However, the obvious downside of the constant feedback is that solving an exercise can become trial-and-error repetition.

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