Introduction:

In the wake of the rapid advancement of digitalization in the classroom, it is important to investigate the effects of different writing instruments on writing and learning processes. Grounded Cognition theories suggest that handwriting might lead to a deeper understanding of content written down, as every letter must be traced according to a unique motor plan [1]. In contrast, other theories postulate that the reduced motor complexity in typewriting, where a letter is produced by pressing a specific key on the keyboard, results in more working memory capacity for other cognitive processes and thus to better memory performance [2].

Research into the effects of hand- vs. typewritten notes on memory retention has so far yielded mixed results [2,3,4]. What is more, although pupils often take notes in class for the purpose of learning and although they increasingly do so using digital devices, writing mode-specific effects on memory performance among children and adolescents have hardly been investigated to date. Additionally, studies have not yet shed light on possible interactions between the kind of retrieval (recognition vs. recall) and the writing mode.

Thus, the present study investigated the influence of the writing mode on memory performance of school-aged children. However, it not only examined learning differences after hand- vs. typewritten notes, but also differences in the process of note-taking and how the mode-specific writing process accounts for differences in memory performance.

Methods:

The interdisciplinary empirical study was based on theories and methods from cognitive science, memory psychology, writing research and language didactics. Recall and recognition performance of 14- to 17-year-old school children (n=45) was tested in a within-subjects design. The adolescents took notes both by hand and on a keyboard while listening to a lecture. After seven days a memory test was conducted. Moreover, the transcription fluency in both writing modalities and the language proficiency were measured in separate tests. Various variables of the writing process were measured, e.g. transcription fluency, note-taking efficiency, verbal overlap, number of information units written down and, for the first time, pause-burst-intervals. In the handwriting condition, digital smartpens and a software called Handspy were used in order to measure transcription fluency and pause-burst-intervals [5]. In the typewriting condition, the keyloggging software Inputlog recorded the writing process.

Results and Discussion:

The investigation revealed fundamental differences in longhand and keyboard note-taking. Moreover, superior memory performance was revealed after handwritten notes, but only in the recall condition. A multiple linear regression analysis revealed that the writing mode, the amount of information written down, age and sex are relevant predictors of test performance. Moreover, results demonstrated that even more of the information units that had not been written down could be recalled in the memory test if the notes had been written by hand.

All in all, the study suggests that handwriting leads to deeper information-processing than typewriting. However, further studies must investigate the role of transcription fluency, as the results might reflect a low level of automation in typewriting, which could put a strain on working memory.

Educational significance:

Note-taking is a central, but under-researched form of writing and learning in classrooms. As the digitalization in education leads to an increase in digital writing, it is important to further investigate the cognitive consequences of the writing mode. Implications for classroom note-taking are discussed, including the need to teach effective note-taking in order to enhance learning outcomes. Finally, the study results suggest that the appropriate writing mode should be selected depending on the objective of the writing activity.

Introduction: Theoretical background, aims, and research questions

For many years, note-taking has been a tried-and-tested strategy to effectively learn from video lectures. These effects are based on note-taking’s main function, namely the active cognitive processing of the learning material (Piolat et al., 2005). However, most of the relevant studies on note-taking’s effectiveness have been conducted in on-site learning scenarios, be it in class or in the lab (e.g., Mueller & Oppenheimer, 2014; Wong & Lim, 2023). It thus remains an open question whether note-taking is also effective in an authentic distance learning scenario. Moreover—speaking of authentic learning scenarios—, one must also consider the ubiquitous smartphones of today. Studies confirm the intuitively obvious effect that smartphone noises disrupt learning (e.g., End et al, 2009; Kaminske et al., 2022). However, these findings again refer to on-site learning scenarios, leaving open the question of similar detrimental effects while distance learning.

The current study addresses these open questions and sets two research goals: (1) To analyze the potential positive effects of note-taking on learning engagement and outcomes and (2) to analyze the potential negative effects of smartphone noises on note-taking and learning outcomes. Unlike previous research though, the current study relies on an authentic distance learning scenario. It resembles a typical university video lecture without any instructional support or potential influence from researchers.

Referring to the first research goal, taking notes should help learners actively process the learning material, even in a distance learning scenario. Therefore, note-taking is assumed to foster learning engagement and outcomes (Hypotheses 1a and b). Furthermore, learning engagement should mediate the potential note-taking effect on learning outcomes (Hypothesis 1c). Finally, referring to the second research goal, smartphone noises are assumed to hinder note-taking (Hypothesis 2a) and to be detrimental to learning outcomes (Hypothesis 2b).

Methods

In the present study, 196 undergraduate psychology students (144 female; mean age = 23.44 years) students learned in an asynchronous online scenario using a 12-min prerecorded video lecture on the topic “Learning strategies.” The field experiment followed a 2×2-factorial design. Factor A referred to whether or not participants received a note-taking request before the video lecture. Factor B referred to whether or not participants were exposed to smartphone noises mixed into the video lecture’s audio track. After the video lecture, participants’ learning engagement was assessed via a self-report scale, and they were asked whether they had actually taken notes. In terms of learning outcomes, participants took a declarative knowledge test about the lecture’s main content.

Results and Discussion

Note-takers (n = 67) reported stronger learning engagement (Hypothesis 1a) and scored higher on the posttest (Hypothesis 1b) than the non-note-takers (n = 129). Furthermore, learning engagement mediated the note-taking effect on learning outcomes (Hypothesis 1c). This result pattern underscores the central importance of active cognitive processes through note-taking for learning outcomes. As for smartphone noises, they indeed reduced the learners’ compliance to fulfill the request to take notes from about 76% to 53% (Hypothesis 2a). Furthermore, smartphone noises impaired learning outcomes of non-note-takers (Hypothesis 2b). By contrast, note-takers with and without smartphone noises performed likewise in the posttest.

Educational Significance of the Research

The educational significance of this research relates to three aspects. First, this study’s findings provide ecologically valid empirical support of the benefits of taking notes while learning from an asynchronous video lecture. These benefits can be effectively achieved by simply asking students to take notes before the video lecture.

Second, smartphone noises have been shown to be a kind of double curse, especially in an unsupervised distance learning scenario. These noises prevent almost a third of learners from using an effective learning strategy, namely note-taking. Furthermore, these noises also have a detrimental effect on the learning outcomes of those learners who do not take notes.

Finally, the similar posttest performance of note-takers with and without smartphone noises suggests that note-taking can serve as a protective factor against smartphone noises. It is therefore advisable to take notes, especially in environments where smartphone noises cannot be avoided. Overall, practitioners and instructors should encourage their learners to take notes and avoid smartphone noises, not only in the lecture hall, but also during distance learning with a video lecture.

Introduction: To date, there is virtually no research on the relationship between active and passive voice and self-evaluation and metacognition. In online self-evaluation tasks, students reflected on the differences between their answers and sample answers. Studies on how active or passive voice affects readability, comprehension, or external grading quality do not demonstrate any effect of voice form on self-assessment. Experimental work on reader comprehension shows that active voice often yields better comprehension and readability in academic texts than passive voice (Uzmi et al., 2024). Rhodes (1997) found that the incidence of passive voice in scientific student lab reports did not affect instructors’ ratings of overall paper quality. However, there are indirect references to a relationship between grammatical voice and self-evaluation: passive constructions increase cognitive distance and abstraction, whereas active constructions promote the agency and immediacy in text processing (Chan, & Maglio, 2020). Active constructions improve readers' comprehension and processing efficiency (Rhodes, 1997). Analgous to this assumed relationship with self-evaluation, there may be an even stronger relationship between active and passive voice and metacognition.

Research questions: (1) Is there a relationship between passive and active voice and self-evaluation? (2) Is there a relationship between passive and active voice and the type of metacognition in self-reflections?

Methods: The sample consists of 2966 self-reflections written by 218 students from two courses at a distance university. The statements were categorised according to types of metacognition, use of active or passive voice and personal form (first vs. third person). The dataset contains students' self-evaluations. Depending on the course, students assessed their answers on a scale from 1 to 7 (Project Management) or from 1 to 9 (Health Psychology). We used Bayesian multilevel analysis (Brückner, 2027) to examine the relationship between self-evaluation and writing style (active vs. passive vs. neutral voice) and a chi-squared analysis to relate writing style with categories of metacognition.

Results: The majority of self-reflections were written in acitve voice (69%), 27% in neutral voice, and 4% in passive voice. The Bayesian multilevel analysis showed no relationship between active (-.04, CI: -.34 to .25) or passive voice (.19, CI: -.28 to .66) and self-evaluation (reference: neutral voice). Overall, the pattern indicates weak, non relevant estimats, with active voice showing values nearly indentical to voice-neutral answers. Passive voice was associated with slightly higher self-evaluations than active or voice-neutral answers, although this effect was weak. The best-fitting model included the use of the first versus third-person forms, which showed a considerable effect on self-evaluation (-.40, CI: -.66 to -.24), with higher grades associated to third-person use. Active and passive writing style was related to metacognitive categories (χ²=106.46, df=12, p<.001). Reflections categorized as Rationale (for errors, omissions, or alternatives), Realizations (of positive or negative aspects of answers), and Misunderstandings (of task or question) were written mostly in active voice (89-96%). In contrast, Explanations (of what is wrong or missing), Mentioning (of errors, omissions, contents, differences, or commonalities), References to form (comments on form of answer) and Other person-related categoreis (justification, uncertainity, learning experience) were less often written in active voice (63-76%).

Discussion: The use of active and passive voice in written self-reflections within self-evluated tasks is related to categories of metacognition but not to self-evaluation. The relationship between active or passive voice and metacogntion indicats a deeper engagement when an active writing style is used. This finding supports the assumption that active voice may lead to richer metacognitive content. It is consistent with the findings of Werlen (2025), who showed that categories of metacognition are associated wiht deeper metacognitive processing when a less distant first-person writing style is used. However, the association between third-person use and self-evaluation could not be established for passive voice, even though passive construction are also perceived as more distant and more abstract than active constructions. Because only 4% of the sentences are written in passive voice, these results should be interpreted with caution.

Educational Significance: For educators, these findings highlight that writing style can serve as an indicator of metacognitive engagement, but should not be interpreted as evidence of higher or lower achievement. Instructional design may benefit from encouraging active constructions in reflective writing to promote deeper reflection and more meaningful engagement with feedback, particularly in online or distance-learning settings.