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This paper describes the effects of a pairing method based on digital textbook logs and learners’ artifacts in conceptual modeling exercises. We developed a digital textbook system called Smart E-textbook Application (SEA) and a conceptual modeling tool called KIfU 3.0 to collect conceptual modeling activity logs in exercises. This study proposes a method that makes pairs of learners for group work by considering the characteristics of the artifacts created by them and digital textbook logs. An initial evaluation was conducted to evaluate the learning effects of our proposed pairing method compared to the random pairing method. From its results, this study found the discussion patterns and digital textbook browsing status in the maximum value of improvement and deterioration points of learners’ artifacts in the conceptual modeling.

Effective interpretation of genome function and genetic variation requires a shift from epigenetic mapping of cis-regulatory elements (CREs) to characterization of endogenous function. We developed hybridization chain reaction fluorescence in situ hybridization coupled with flow cytometry (HCR-FlowFISH), a broadly applicable approach to characterize CRISPR-perturbed CREs via accurate quantification of native transcripts, alongside CRISPR activity screen analysis (CASA), a hierarchical Bayesian model to quantify CRE activity. Across >325,000 perturbations, we provide evidence that CREs can regulate multiple genes, skip over the nearest gene and display activating and/or silencing effects. At the cholesterol-level-associated FADS locus, we combine endogenous screens with reporter assays to exhaustively characterize multiple genome-wide association signals, functionally nominate causal variants and, importantly, identify their target genes.

In ubiquitous learning, authentic experiences are captured and later reused as those are rich resources for foreign vocabulary development. This article presents an experiential theory-oriented approach to the design of learning analytics support for sharing and reusing authentic experiences. In this regard, first, a conceptual framework to support vocabulary learning using learners' authentic experiences is proposed. Next, learning experiences are captured using a context-aware ubiquitous learning system. Finally, grounded in the theoretical framework, the development of a web-based tool called learn from others (LFO) panel is presented. The LFO panel analyzes various learning logs (authentic, partially-authentic, and words) using the profiling method while determining the top-five learning partners inside a seamless learning analytics platform. This article contributes to the research in the area of theory-oriented design of learning analytics for vocabulary learning through authentic activities and focuses on closing the loops of experiential learning using learning analytics cycles.

Both upregulation and downregulation by cis-regulatory elements help establish precise gene expression. Our understanding of how elements repress transcriptional activity is far more limited than activating elements. To address this gap, we characterized RE1, a group of transcriptional silencers bound by REST, on a genome-wide scale using an modified massively parallel reporter assay (MPRAduo). MPRAduo empirically defined a minimal binding strength of REST required for silencing (REST m-value), above which multiple cofactors colocalize and act to directly silence transcription. We identified 1,500 human variants that alter RE1 silencing and found their effect sizes are predictable when they overlap with REST binding sites above the m-value. In addition, we demonstrate that non-canonical REST binding motifs exhibit silencer function only if they precisely align two half sites with specific spacer length. Our results show mechanistic insights into RE1 silencer which allows us to predict its activity and effect of variants on RE1, providing a paradigm for performing genome-wide functional characterization of transcription factors binding sites.