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Background: Digital texts are progressively becoming the medium of learning for students, but research has indicated that students tend to process information more superficially while reading on screen. It is therefore relevant to examine what strategies can support digital text comprehension. Objectives: This study aimed to investigate the effects of highlighting—both learner generated and experimenter provided—when reading digitally. Methods: University students (N = 170) were randomly assigned to the condition of learner‐generated highlighting, experimenter‐provided highlighting, or control. Reading outcomes were measured as literal and inferential text comprehension, transfer of knowledge, and metacognitive calibration of comprehension performance at immediate and delayed post‐tests. Individual differences in prior knowledge, cognitive reflection, and reading self‐efficacy were taken into account. The quality of the information highlighted by students in the condition of active highlighting was also measured. Results: From linear mixed‐effects models, the main effect of condition did not emerge for any of the outcomes. However, an interactive effect of condition and cognitive reflection emerged for literal text comprehension that favoured readers in the condition of experimenter‐provided highlighting with higher ability to resist automatic thinking. Inferential text comprehension, transfer of knowledge, and calibration of performance were only predicted by cognitive reflection or reading self‐efficacy. Finally, the quality of information highlighted significantly contributed to students' literal text comprehension and transfer of knowledge in the learner‐generated highlighting condition. Takeaways: Active highlighting is not effective per se during digital reading. The "amplification" effect of already highlighted text and higher cognitive reflection suggests that readers who are more able to resist automatic thinking may also invest more effort in the task, taking more advantage of the provided support. Even if active highlighting may not be effective per se compared to other reading conditions, what students highlight contributes to literal text comprehension and their learning from text. Lay Description: What is already known about this topic: Reading on a screen to learn new content for academic assignments is a typical learning activity for todays' students.For digital reading students tend to adopt a more superficial approach compared to traditional reading.Reading strategies can help readers to comprehend complex digital texts.Highlighting is a technically simple strategy to use for digital reading, which can at least support the selection of relevant information for text comprehension. What this paper adds: When considering digital reading neither learner‐generated highlighting nor experimenter‐provided highlighting is effective by itself.Experimenter‐provided highlighting is more effective for literal text comprehension than learner‐generated highlighting when combined with readers' ability to cognitively reflect and resist more automatic responses.The individual differences of cognitive reflection and reading self‐efficacy predicted inferential text comprehension and transfer of knowledge.Reading self‐efficacy also predicted readers' metacognitive calibration of comprehension performance (accuracy of self‐judgements of comprehension performance). The implications of study findings for practitioners: The effective use of highlighting for text comprehension probably requires some explicit instruction and practice in learning devices.The potential benefits of either learner‐generated highlighting or experimenter‐provided highlighting should be considered in relation to cognitive and motivational individual differences.It is important to create or refine readers' awareness that the quality of the information they highlight matters as it is associated with their text literal text comprehension and transfer of knowledge. [ABSTRACT FROM AUTHOR]

Integrating multiple representations into a coherent mental model is one of the challenges when learning with multimedia. In this experimental study (N = 173), we examined how highlighting corresponding information in text‐graph learning material can help higher education students to make the necessary connections and improve learning outcomes in two domains. We compared a control condition to a signaling condition with given highlights and an active signaling condition where students were asked to visually highlight corresponding text and graph information themselves. There was no overall benefit of given signals or active signaling. We discuss prior knowledge and the quality of learner‐generated signals as possible explanations. For biology learning material, prior knowledge moderated the effect of active signaling. In economics, the effect of prior knowledge was mediated by the quality of learner‐generated correspondences. Our findings suggest that different methods of supporting text‐graph integration might be useful for different students and learning material. [ABSTRACT FROM AUTHOR]

This paper introduces a realization method for curved in Cell TFT‐LCD MDL, and for the 15.6inch module, the surface of the in cell touch module is designed to meet the high‐end vehicle streamlined concept by special technical means. At the same time, the room space is saved to a certain extent. The paper introduces the realization method of the true surface design of the backlight and the display effect of the bright backlight by highlighting the film structure. This paper also introduces a new UI(User Interface) vehicle interface design, which greatly improves the ride experience for the driver and passenger. [ABSTRACT FROM AUTHOR]