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In two-stage exams, students complete some academic work individually then immediately complete the same work again in small groups. Prior research shows that students perceive the dialogue they engage in at the group stage aids their individual learning. Outcome studies also suggest that engaging in dialogue benefits individual student learning. This investigation takes a different perspective. Learning during two-stage exams is conceptualised as deriving from internal feedback processes, with comparison seen as the mechanism that fuels these processes. To investigate this interpretation, students were asked to write a feedback commentary on their own learning during a two-stage exam. The intention was to make explicit the internal feedback that would naturally occur during the group stage, as students compare their earlier individual performance against the group dialogue and the unfolding group output. Analysis showed that students not only generated content and process feedback but also self-regulatory feedback, and that this feedback was of a higher quality than, and went beyond, the comments the teacher provided. This investigation yields new insights into the nature, scope and power of internal feedback processes, and into how to improve students' learning during such two-stage sequences.

This article explores peer review through the lens of internal feedback. It investigates the internal feedback that students generate when they compare their work with the work of peers and with comments received from peers. Inner feedback was made explicit by having students write an account of what they were learning from making these different comparisons. This allowed evaluation of the extent to which students' self-generated feedback comments would match the feedback comments a teacher might provide, and exploration of other variables hypothesized to influence inner feedback generation. Analysis revealed that students' self-generated feedback became more elaborate from one comparison to the next and that this, and multiple simultaneous comparisons, resulted in students' generating feedback that not only matched the teacher's feedback but surpassed it in powerful and productive ways. Comparisons against received peer comments added little to the feedback students had already generated from comparisons against peer works. The implications are that having students make explicit the internal feedback they generate not only helps them build their metacognitive knowledge and self-regulatory abilities but can also decrease teacher workload in providing comments.

Students generate internal feedback by comparing their current knowledge against some reference information. That information might be planned for by teachers -- usually as comments on students' performance -- although most information is accessed by students themselves during task engagement, from their interactions with others, with resources and from memories of prior performances. Nearly all research on feedback in higher education focuses on comments as the comparison information. Ongoing and natural feedback comparisons with other information sources have been neglected: hence their potential for learning remains unexplored. To unlock the power of internal feedback, teachers need to have students turn some natural comparisons that they are making anyway, into formal and explicit comparisons and help them build the capacity to exploit their own comparison processes. To envision the possibilities, I present a new model of how students generate internal feedback as they self and co-regulate their learning, using information from multiple sources. I also synthesise two bodies of research to show how comparisons with different kinds of information, singly and in combination, can alter the nature and quality of the internal feedback that students generate. This lens of comparison changes everything. It calls for a fundamental shift in feedback practices and research.

Prostate cancer represents a substantial clinical challenge because it is difficult to predict outcome and advanced disease is often fatal. We sequenced the whole genomes of 112 primary and metastatic prostate cancer samples. From joint analysis of these cancers with those from previous studies (930 cancers in total), we found evidence for 22 previously unidentified putative driver genes harboring coding mutations, as well as evidence for NEAT1 and FOXA1 acting as drivers through noncoding mutations. Through the temporal dissection of aberrations, we identified driver mutations specifically associated with steps in the progression of prostate cancer, establishing, for example, loss of CHD1 and BRCA2 as early events in cancer development of ETS fusion-negative cancers. Computational chemogenomic (canSAR) analysis of prostate cancer mutations identified 11 targets of approved drugs, 7 targets of investigational drugs, and 62 targets of compounds that may be active and should be considered candidates for future clinical trials.