Autonomous learning, also named self-managed learning (Lizzio & Wilson, 2005), self-regulated learning (Boekaerts & Niemivirta, 2000; Entwistle & McCune, 2004), or self-directed learning (Garrison, 1997), denotes a process where students possess sufficient opportunity to organize and regulate their own learning. In autonomous learning, students’ learning efficiency may vary with the learning contents (or concepts) and time epochs during a day. Previous studies have suggested that there exists a time-of-day effect in one’s learning efficiency. For example, young participants showed superior memory accuracy in the afternoon compared to that in the morning (Hourihan & Benjamin, 2014). When identified as morning type or evening type, students reported differences in memory, interest, motivation, and achievement across time of day (Anderson et al., 1991; Itzek-Greulich et al., 2016). In the field of logical reasoning, students’ speed and performance improved markedly from morning to noon, and subsequently performance fell off rapidly from afternoon to night (Folkard, 1975).
Several other factors influence cognitive performance and thus contribute to intra-individual variance in learning efficiency. Most importantly, the circadian rhythms in cognitive processes affect school related activities such as attention and memory (Valdez, 2019). Circadian rhythms have been found in three basic cognitive processes which are interconnected with each other: attention, working memory and executive functions. Attention denotes to the act of selectively concentrating on a discrete aspect of information while ignoring other perceivable information, which also interacts effectively with the environment (Nobre & Kastner, 2014); working memory comprises the storage, retrieval, and use of information (Baddeley & Hitch, 1974); and executive functions refer to the ability to program or regulate behavior, which is essential for problem solving, decision making as well as self-control (Lezak et al., 2012). These cognitive processes roughly reach their lowest levels during nighttime and early morning, improve around noon, and are at even higher levels during afternoon and in the evening (Valdez et al., 2005; Ramírez et al., 2006; García et al., 2012; Gallegos et al., 2019), demonstrating their coordination with the physiological rhythms of the human body (Valdez, 2019).
The learning efficiency and time course of cognitive performance are nonetheless also modulated by other conditions. The “chronotype” is a reproducible and stable trait of individuals, which encompasses morning type, evening type and intermediate type according to preferred time of learning or other activities (Kantermann & Eastman, 2018). The morning and evening types show an advance or a delay in cognitive rhythms respectively (Roenneberg et al., 2003). Hunger and sleep deprivation influence the rhythms of cognitive performance as well (Benau et al., 2014; Killgore & Weber, 2014), leading to the impairment of attention, memory and executive functions (Benton & Parker, 1998; Doniger et al., 2006; Krause et al., 2017). The fluctuations of attention interrelate with emotion (Taylor & Fragopanagos, 2005), and certain emotional states appear to facilitate memory control (Engen & Anderson, 2018) and decision making (Woodcock et al, 2020). Finally, exposure to environmental noise deteriorates cognitive performance with regard to comprehension, memory, attention, as well as executive functions (Clark & Paunovic, 2018).
A classical theory of learning skills is the “Building Blocks of Learning” (Goldstein & Mather, 1998). Learning skills are regarded as comprising ten building blocks that reflect foundational skills, symbolic or perceptual processing skills, and conceptual or thinking skills, including attention and self-regulation, emotion, behavior, self-esteem, phonological processing, orthographical processing, motor processing, thinking with language, thinking with images, and thinking with strategies (Abu-Hamour, 2014; Goldstein & Mather, 1998). In the current study, we would separate the learning contents into seven elements, i.e., language learning, problem analyzing, memorizing, comprehension, logic thinking, drawing and writing.
There are several inventories that have been developed to assess learning efficiency and learning style (Horne & Östberg, 1976; Fleming & Mills, 1992; Duff, 2004; Fleming, 2001; Roenneberg et al., 2003). To date however, there is no single study which illustrates a structure-validated questionnaire that investigates the optimal time for autonomous learning of different leaning-contents. There is no standard division of time epoch of a day either. Some scholars preferred to use the exact scales of time epoch (e.g., 04:00-07:00 AM or 8:00-11:00 PM) when measuring the rhythms of cognitive performance (Garcia et al., 2012; Ramirez et al., 2006; Valdez et al., 2005). However, cognitive performance and learning efficiency fluctuate with numerous factors within different individuals (Benau et al., 2014; Roenneberg et al., 2003; Taylor & Fragopanagos, 2005), and the exact time points may not be suitable for all participants when referring to the automatic learning.
In the current study, we adopted both exploratory and confirmatory factor analyses in order to validate the structure of a questionnaire. Furthermore, we roughly divided the day into the early morning, morning, noon, afternoon, nightfall, evening, and the late night. On the other hand, based on previous studies, the optimal time of learning refers to the time epoch when an individual is highly focused, does not easily fatigue, stable in mood, fluent in thinking, and with the highest learning efficiency. Thus, we recruited a group of university students to answer a matrix of statements regarding the optimal time for autonomous learning. Considering that different cognitive performances show similar rhythms in a day (Valdez, 2019), we hypothesized that the efficiency of autonomous learning is primarily affected by the time of day instead of the learning contents.