This section is comprised of four subsections, including English teachers’ feedback in the cloud classroom, learners’ emotional perceptions and attitudes towards English teacher’s feedback, and research questions.
2.1 Teacher feedback in the cloud classroom learning environments
Feedback, referred to ‘information provided by an agent about personal performance or understanding’, has been perceived as one of the strongest effects on foreign language learning processes (Harks et al., 2014). For decades, teachers’ feedback has been deemed as one of the most persuasive effects on English learning as it could scaffold students’ cognitive development, focus on their weaknesses and strengths, and provide a suggestive solution for better performance (Vøtty & Smith, 2019). Teachers’ beliefs about their feedback would affect online classroom environments from physical, emotional, and pedagogical dimensions (Vøtty & Smith, 2019; Closs et al., 2021). Despite feedback has a positive impact in general, not all types of feedback are fairly effective. Negative feedback makes learners more likely to be lost in the online learning environments than in the traditional one, whereas positive feedback would exert a significant effect on students’ learning motivation. Feedback should, however, be regulated, ordered, and structured in a particular way to stimulate student engagement (Harks et al., 2012).
Differences in feedback may exist in offline and online learning environments. Online feedback is more positively accessed by students, and thus more probable to impact their emotions and further learning than in the face-to-face mode (Moffitt et al., 2020). Therefore, teachers could include feedback in teaching plans and elaborate in detail from the following aspects: who should receive their feedback, how and when it is best given, which learning environment it should be delivered, what it should contain, and why it should be delivered (Brown et al., 2012; Moffitt et al., 2020).
Many researchers have focused on feedback effects on error correcting and learning achievement (Brown. et al., 2012). Some have concerned motivational effects (McGarrell & Verbeem, 2007; Narciss, & Huth, 2006), and a few have explored students’ self-perception (Zhang, 2020; Patti et al., 2021). The impact of teachers’ feedback on students’ emotional perception in online language learning environment remains less explored. This study is thus an effort to embark on exhaustive research that assesses high school students’ emotional perception and attitudes in cloud classroom learning environment.
2.2 Students’ emotional perception of feedback
It is becoming increasingly difficult to ignore students’ emotional perceptions in cloud classroom learning environments. Krashen (1985) proposed that in the process of language acquisition, learners should not only ensure sufficient comprehensible input, but also focus on the impacts of emotional factors. Krashen interpreted, in general, the affective factors included motivation, attitude, self-confidence and inferiority. If learners were motivated, confident and had no inferiority or negative emotions during language acquisition, the comprehensible input they received would promote target language acquisition or vice versa. There are five scales in this research based on the affective filter hypothesis, namely, perceived teaching practice, learning motivation, self-confidence, interest, and inferiority. This study is committed to testing whether learning motivation, self-confidence, interest, and inferiority can function as indicators and predictors of perceived teaching feedback in cloud classroom learning environments.
2.2.1 Learning motivation
Learning motivation, the director of students’ learning behavior, has always been perceived as a strong indicator and contributor to students’ learning success (Narciss, & Huth, 2006). In this case, many studies have investigated the positive correlations between student motivation and their educational achievement (Javad & Shahnaazari, 2020; Yu et al., 2020). Moreover, motivation is the principal reason to promote learners to engage, perform, and persist in a learning environment (Yu, 2019, Javad & Shahnaazari, 2020; Yeager & Dweck, 2020). Nevertheless, learning environments can impact students’ motivational processes and performance (Dale & Maria, 2020), and online learning environments are hypothesized to enhance students’ motivation than conventional learning environments (Teklu, 2010; Yu, 2020). Internet-supported learning environments could be identified as a motivation source by its friendliness and usefulness (Patti, 2021). The synchronous and asynchronous learning environments can help students to complete tasks, acquire knowledge, and interact online (Teklu, 2010; Yu, 2019). In this study, motivation is, therefore, perceived as a significant emotional predictor of teaching feedback practice.
2.2.2 Self-confidence
Confidence can be viewed as state-like and it changes over time based on tasks, and students of different ages need self-confidence (Fleeson, 2007; Brown et al., 2016). Being self-confident enables students to do more autonomous thinking and finally benefit their learning satisfaction and outcomes (Roh et al., 2013; Yu, 2019). Recent studies have indicated that attitudes towards online learning environments can positively predicate students’ self-confidence in many different ways from critical thinking, error-correcting, performance, and activities engaging (Hong et al., 2017; Yu, 2019). Self-confidence, however, obtained from internet learning environments, can be fairly consistent in showing prediction of progress in critical thinking (Hong et al., 2021). Thus, the study intends to examine the effects of self-confidence on perceived teaching feedback in cloud classroom learning environments.
2.2.3 Interest
Online learning environment has been catching learners’ increasing interest since it appeared (Wu et al., 2018). Interest is generally referred to as a motivational variable that could arouse individuals to engage in learning activities (Renninger & Hidi, 2011). Interest has been coupled with positive results. In other words, interest-driven learners are more likely to concentrate on, persist, and commit to activities in learning context (Saba et al., 2019). Interest is central in technology-based learning environment and positively correlated with learning achievement (Wu et al., 2018). Besides, technology-based learning environments provide a platform allowing numerous students to acquire and share knowledge, cooperate, and interact with peers online. Students with higher interest might find learning enjoyable and perceive teachers’ feedback positively in cloud classrooms. (Harks, 2014; Haro, 2019).
2.2.4 Inferiority
Negative self-conception is also one of the essential factors influencing individual mal-adjustments (Alexander et al., 1957). Alder was also the first researcher who put forward the theory of inferiority, where he stated that inferiority was a crucial driving force associated with personal development. Inferiority was manifested among top students as low self-evaluation, sensitivity, and self-concealment (Karimi, 2016). Based on cognitive theories (Beck, 1967), inferiority is inseparable from the individuals’ self-evaluation and it is unavoidable in the emotional perception of teaching feedback. Previous studies explored students learning on screen and found screen inferiority from performance and overconfidence which inferiority could be overcome by different media environment, feedback practice, and qualitive guidance and regulation (Lauterman & Ackerman, 2014).
2.3 Students’ attitudes towards feedback
Many studies have diverted their focus from the effectiveness of teachers’ feedback to students' attitudes towards feedback in L2 (Li et al., 2015; Moffitt et al., 2020). Previous studies on university students’ attitudes reveal that online feedback couldn’t act as a learning aid although it can promote evaluation processes (Wen & Tsai, 2006; Kormos & Csizér 2008). Gender differences in attitudes towards feedback also exists in online contexts where males had more positive attitudes than females do (Kormos & Csizér 2008). Moreover, Chen and Cheng (2008) have concluded that students are disappointed with feedback because it does not help improve their performance. However, according to Li et al. (2015), 18 out of 27 students expressed high satisfaction, which pointed out evaluators’ corrective feedback works. Varying attitudes toward feedback may be responsible for different tasks, research designs, and student groups.
Students’ attitudes towards feedback are also identified to be influenced by environmental factors (Denton et al., 2008). English as foreign language (EFL) learners do not study in a decontextualized environment. Rather, they are often faced with multiple after-class assignments from different course teachers simultaneously (El Ebyary & Windeatt, 2010). Students usually prioritize their learning tasks based on their learning beliefs (Suzuki et al., 2019). In this way, their attitudes towards feedback are prone to be affected by environmental factors, such as teacher stance, workload and educational methodology (Zhang, 2020).
In this study, students’ attitudes integrated with emotional perceptions are explored in cloud classroom learning environments.