What stands out as interesting is that most of these apps could be classified as Web 2.0 technology, i.e., a “second generation, or more personalised, communicative form of the World Wide Web that emphasises active participation, connectivity, collaboration, and sharing of knowledge and ideas among users” (McLoughlin & Lee, 2007, p. 665). This is in line with previous empirical findings that also revealed the suitability of Web 2.0 tools for DI (e.g., Güvenç, 2021; Hustinx et al., 2019; Meşe & Mede, 2022; Rapti, 2018) and corroborates Zeng (2020), as well, who highlighted the potential of Web 2.0 tools in making learning more personalised for students. These findings suggest that within the technological repertoire available to teachers for DI, Web 2.0 tools appear to be an especially promising avenue for differentiation.

In the present section, these tools are discussed with respect to how they may be used to differentiate the content, process, and product of teaching according to students’ readiness levels, interests, and learning profiles, as well for fostering a motivating and collaborative learning environment conducive to differentiated teaching.

 

Another type of technology suitable for content differentiation, not explicitly mentioned in the interviews but observed in some of the lessons I visited, was online learning resources with scaffolding potential, such as the British Council’s LearnEnglish Teens website, YouTube, and online dictionaries. In Bea’s class, students had the chance to listen to the recording on the British Council website with or without the transcript, as did the students in Bea’s, Rebeka’s, and Tímea’s classes when watching YouTube videos. Furthermore, in Emma’s and Tímea’s classes students could use online dictionaries that gave them immediate help in checking unknown words. What unites these online resources is their potential to be used for scaffolding, i.e., for providing additional support or challenges to help students access core learning material effectively, which is another important content differentiation strategy (Tomlinson & Allan, 2000).

These two types of technologies, i.e., tiered reading applications and online learning resources with scaffolding potential, could be regarded as easily accessible, ready-to-use content differentiation tools that are “pitched at different reading levels, complexity of vocabulary or support” (Blaz, 2016, p. 10). The variety provided by these tools could help student engage with the content within their zone of proximal development (Vygotsky, 1978)—a fundamental principle of DI (Roiha, 2014; Subban, 2006), where they are challenged and supported appropriately, and, as a result, are given equal chance to develop and experience flow (Csíkszentmihályi, 1990).

 

Interactive activity sets could also help students engage with the content in different ways. Canva, Genially, Classcraft, and Nearpod, for example, can be used as platforms that direct students to online activities of different difficulty levels and learning modes, and as such, they can serve as easily accessible digital versions of the tic tac toe board and learning centres, i.e., two of the most well-known DI strategies (Blaz, 2016). This practice was also observed in Rebeka’s class, where Nearpod was used as a platform where students could complete a sequence of activities at their own pace, in line with their learning preferences and readiness levels (for an overview of the Nearpod slides, see Appendix P3).

The multi-faceted support that these vocabulary apps and interactive activity sets offer for learners is in line with the claims of various authors who suggest that the different modes of practice provided by technology can help students make sense of the content in various ways (Benjamin, 2014; Richards, 2015; Stanford et al., 2010). As Tomlinson and Allan (2000) point out, the main objective of DI is to provide flexibility in task complexity so that “varied students find learning a good fit much of the time” (p. 7). The process differentiation tools identified in the examined research contexts appear to enhance this flexibility by providing platforms for creating and sharing multimodal and multi-level activities.

 

As for gamification tools, the findings of the interviews and observations corroborate each other in several respects. Firstly, results suggest that quiz games such as Kahoot, Gimkit, Quizizz, and Quizlet Live have the potential to enhance student engagement and motivation. During the interviews, the participants emphasised that these games “engage students in a gamified way” (Bea), which was also observed in Bea’s and Rebeka’s classes, where students appeared to be excited and active when playing these quiz games. Besides, through team mode and random grouping, some quiz games can promote flexible grouping and teamwork, as well, which are central to a differentiated learning environment (Blaz, 2016; Tomlinson, 1999; Tomlinson & Allan, 2000). For example, as Kamilla explained, quiz games such as Quizlet Live generate random groups, which invites students to work with a variety of peers during the competitions. As such, this function supports flexible grouping, which, as Tomlinson and Allan (2000) point out, is an important DI component as it provides “a chance for students to see themselves in a variety of contexts, and rich assessment data for the teacher who ‘auditions’ each learner in a wide range of contexts” (p. 6).

ClassDojo, Classcraft, and Motimore are examples of comprehensive gamification platforms that could also prove helpful in establishing a learning environment conducive to DI. As Kamilla and Rebeka pointed out, the main benefit of these platforms is that they promote a positive classroom climate through a reward system that encourages good classroom behaviour and diligence. The ‘Kudos’ function of Classcraft, for example, encourages students to write positive messages to each other, while the customizable avatars in Classcraft and ClassDojo motivate students to work hard to collect points. This confirms the proposition put forward by Barbarics et al. (2019), who argue that the opportunity for students to see their development through a customizable avatar fosters a sense of ownership and engagement, since “the more one feels that something is their own, the more they feel attached to it” (p. 17). In Kamilla’s and Rebeka’s lessons, this was observable as every student participated in the classroom activities and was eager to collect points.

The use of Minecraft in Emma’s class is an example of game-based learning, where the learning process is integrated into an actual game (Dimitra et al., 2020). While I did not have the chance to observe the students playing this game, based on what Emma disclosed to me in our interview and post-lesson discussion, as well as on the student samples I received after the lesson, Minecraft appears to be an effective tool for providing a digital space for students to learn in a virtual world. The student samples, such as the theme parks and spa centres built as part of the Summercraft holiday resorts (see Appendix P4), are examples of how game-based learning can foster students’ creativity, which is an important affordance of video games in educational settings. As Lajtai (2022) claims, “well-designed video games engage learners’ creativity with ever-increasing challenges and values their cognitive, aesthetic and emotional investment with rewards” (p. 38). The use of Minecraft in Emma’s class may be considered as an example of how students can engage in creative projects that are meaningful and stimulating on these cognitive, aesthetic, and emotional levels.

Overall, the above ICT tools appear to bear the characteristics of gamification and game-based learning identified by Barbarics (2023), Dimitra et al. (2020), Lajtai (2022), Moyle (2012), and Wiggins (2016), such as providing instant feedback, varied challenges and customised rewards, as well as creating a narrative, all of which contribute to a learning environment conducive to DI. Besides, the collaborative features of these tools, such as the team mode in Quizlet Live and the Kudos function in Classcraft, seem to promote community-building, which is an important feature of gamified learning (Barbarics et al., 2019) and is also a crucial aspect of DI. As Blaz argues (2016, p. 27):

Firstly, readiness levels, and especially students’ English language proficiency in this respect, may be addressed through tiered readings apps such as Kids A–Z and BOOKR. As these apps offer texts at various levels of difficulty, students can engage with the same topic at their own proficiency levels, and once they have completed a particular reading, they can level up and proceed with more difficult passages. Online learning resources with scaffolding potential, such as YouTube, the British Council’s LearnEnglish Teens website and online dictionaries, address differences in readiness levels, too, as they provide transcripts, subtitles and vocabulary support to those who need extra help. Besides, certain process differentiation tools such as Quizlet also resonate well with readiness levels: as Bea pointed out, the different activities generated by the application are of different difficulty levels, and students can choose the activity based on the level of challenge they would like to aim for, which is an important way of differentiating a gamified learning experience (Barbarics, 2017).

Besides, the interactive activity sets, such as Rebeka’s Nearpod activity sequence, allow for readiness-based DI by giving students the option to proceed at their own pace through a sequence of activities, often of increasing difficulty. This is in line with Barbarics et al. (2019), who identified one of the affordances of technology in helping students learn by ‘baby steps’. As explained by Fekete (2018), this approach refers to introducing only one new challenge to the student at a time, then allowing them time to practice. As he argues, this way, “those who already possess the relevant skills and knowledge can easily breeze through the practice tasks – quickly climbing the baby steps. At the same time, beginners can practice the problematic tasks without the risk of failure” (p. 42). Rebeka’s views, stating that “Everyone has a sense of achievement, because in 40 minutes some students will complete three stations and others will complete five, but nobody is bored”, may be considered as an example of such ‘baby steps’ taken in TEDI settings.

The support provided by these tools in offering differentiated content and activities is especially significant considering that previous research (Öveges & Csizér, 2018; Zólyomi, 2022) as well as the results of Phase 1 of this research project (see Section 4.1) have shown that EFL teachers are highly concerned about catering for their students’ readiness, partly due to the mixed proficiency levels present in many EFL classes (Öveges, 2018). Technology may be able to assist teachers in dealing with this issue by providing content and activities that are in line with their students’ proficiency levels and are thus appropriately challenging for them.

Learning profiles can also be catered for with various ICT tools. Both the teachers’ accounts and the observed lessons revealed some technologies that have the potential to address students’ learning profiles through what Benjamin (2014) described as “the interplay and possibilities of learning through words, images, and sounds” (p. 6). For example, content differentiation tools such as Kids A–Z, YouTube, and the British Council’s LearnEnglish Teens website present content in different formats, and as such, can provide students with multiple channels for familiarising themselves with the material. Process differentiation tools such as the interactive slides in Nearpod and the multimodal flashcards and games in Quizlet also address learning profiles as they provide students with opportunities to practice the material in different ways. Flip and Canva, i.e., product differentiation tools, appear useful in this respect, too, as they can help students demonstrate their learning in various audiovisual and written formats. These all confirm Smith and Throne’s (2007) argument that “technology easily complements activities based on multiple intelligences and allows teachers to support students with distinct learning profiles” (p. 70). It is important to note, however, that these “distinct profiles” (Smith & Throne, 2007, p. 70) are not rigid categories in the teachers’ practices. Instead, teachers assess learning profiles informally, i.e., they rely on experiential knowledge rather than formal diagnostic tools and use these insights to offer students a range of learning options rather than prescribing a single activity. This flexible approach is in line with Tomlinson’s (2017) perspective that students should be encouraged to explore different ways of learning rather than being confined to a single method. The choices offered by many of these tools further reinforce this adaptability by letting students engage with learning in ways that best suit their individual learning profiles.

Finally, students’ interests may also be addressed with certain ICT tools. For example, when using reading apps such as Kids A–Z and BOOKR, students can choose from a wide selection of texts, while in Classcraft, they can customise their avatars according to their personal preferences. Furthermore, when using Canva and PowerPoint, they can present their ideas with the help of an extensive collection of templates: in Emma’s genius hours and Kamila’s individual student projects, for example, students used these tools to present on topics of their interests. As the teachers disclosed, and in line with the literature, such projects can act as individualised challenges that foster autonomy (Alqahtani, 2021; Barbarics, 2017; Güvenç, 2021; Meşe & Mede, 2022; Coke, 2018), which is a defining pillar of DI (Blaz, 2016; Tomlinson, 2004).

It is important to highlight that the ability of these tools in responding to learners’ differences was not only reported by the teachers and observed in their lessons but, based on the results of the student questionnaire, are noticed by the students as well. The descriptive statistics of the scales measuring students’ perceptions of the various dimensions of TEDI indicate that students perceive the use of ICT tools as responsive to their readiness (M = 4.27, SD = 0.66), learning profiles (M = 4.14, SD = 0.76), and interests (M = 3.97, SD = 0.77) as these perceptions all had a mean value higher than moderate. This is especially noteworthy considering that, as Blaz (2016) argues, “students are usually responsive to a classroom that they know has been designed for them” (p. 26). The fact that students see that TEDI caters for their needs and preferences could be an important factor in enhancing their language learning experience and strengthening their beliefs in their capability to complete language-related tasks successfully. This connection has been identified in previous qualitative studies (Güvenç, 2021; Hustinx et al., 2019; Vargas-Parra et al., 2018) and has also been statistically proven in the present research project, a detailed discussion of which is presented in Section 4.2.4.2.4.

 

Furthermore, certain apps, such as ELSA, Quizlet, BOOKR, and Kids A–Z, have built-in adaptive technology that also enhances self-paced learning as they adjust questions in real time in response to the students’ answers. However, as Kerr (2016) argues, it is important to see that adaptive learning is more useful for practicing structured content at lower levels, but it may not be suitable in the case of more complex and creative language practice. I could observe this limitation in the present studies, as well, where adaptive technology was primarily used for practicing structured content such as vocabulary, while for higher-order language practice, technology was more often used as a supportive tool (e.g., for designing written products in Canva) rather than as a generator of tasks.

 

Firstly, certain apps provide choice in content. Kamilla, for example, is highly appreciative of BOOKR, as it offers a wide range of books, and students can choose what book to read based on their interests and language proficiency. Choice in content was also observed in the lessons: in the British Council and YouTube listening activities in Bea’s and Rebeka’s classes, for example, students could choose whether to read the subtitles and transcripts or not, and this way they could, to a certain extent, tailor-make the content for themselves (Benson, 2012).

Some ICT tools also provide choice in terms of the activity type. As was highlighted in the interviews, some vocabulary apps, such as Educandy (Bea) and Quizlet (Bea, Kamilla, Rebeka), have built-in features that generate different activities based on a single set of content. Choice in activity types was observed in Tímea’s class, too, where students were given the chance to collect arguments for the debate by coming up with their own or by doing a sorting activity on LearningApps.

Finally, choice in the demonstration of learning can be facilitated with ICT, too, with content creation tools such as Canva and Flip. When designing a resort in Minecraft, developing CVs for Western-themed characters in Canva and creating video summaries of a reading assignment in Flip, students had the chance to decide which templates to use, in line with their interests and preferences. Based on what the teachers shared with me as well as what I observed in the lessons, the students were motivated by the open-endedness of these activities and the variety of templates available to them and were eager to complete the assignments with these tools.

What ties these uses of ICT together is the active participation of students in the decision-making process, which is a fundamental aspect of DI (Tomlinson, 2001). The choices offered by the above ICT tools in terms of content complexity, activity types, and ways of demonstrating learning feed well into this principle as they allow students to decide “when, what, and how to learn based on their own proficiency levels, goals, and learning styles” (Zeng, 2020, p. 26). As such, it fosters self-differentiation as it lets students “actively decide for themselves what level of challenge, engagement, etc., they want, and choose accordingly” (Tennant, 2017, p. 6).

 

Task privacy was both reported on and observed in the participants’ lessons. Bea pointed out that when students use digital apps, they often do not realise that they work on different tasks, while in the case of paper-based worksheets these differences are explicit. This affordance was observed in the lessons of Bea, Rebeka, Emma, and Tímea, as well, where the students were using PCs and mobile phones independently and seemed to be immersed in their activities rather than observing what their peers were doing. The task privacy offered by these tools appears as a solution to what Benjamin (2014) identifies as a “thorny problem” in DI, i.e., the protection of “the self esteem of the student who is working on the least sophisticated task” (p. 5).

Secondly, although not explicitly mentioned in the interviews, during the observations, the privacy of results emerged as another important aspect enhanced by TEDI. Even when students do work on the same activities, their results may be significantly different. As Bandura (1997) argues, comparing achievements to those of peers (vicarious experiences) can impact students’ beliefs in their abilities to perform well; this effect may be positive, if students perform better than others did, but it may also have a diminishing effect if they performed worse than their peers (Lee & Bong, 2023). In the classes observed, students received automatic feedback from certain websites and apps they were using (e.g., British Council’ LearnEnglish Teens, Nearpod, LearningApps), and thus had the opportunity to check their progress privately and work without the pressure of being compared to their peers. If they did not understand something based on the feedback, they could ask for help from the teacher, which they did so, for example, in Bea’s, Tímea’s, and Rebeka’s lessons.

As has been discussed in the interview and questionnaire studies of Phase 1, the issue of students’ feelings of exclusion due to differences in tasks or results is a critical concern of teachers when implementing DI (see Sections 4.1.1.2 and 4.1.2.2, respectively). The most important steps that teachers can take in this respect is to acquaint students with the principles of DI, build trust among them (Blaz, 2016), and create tasks “that look—and are—equally interesting, equally important, and equally engaging” (Tomlinson & Allan, 2000, p. 12). Nevertheless, even when these steps are taken, the ability to work independently on tasks and receive private feedback could encourage students to engage more fully in their learning, and the examined technologies appear to be useful tools in creating such circumstances.

 

Firstly, in line with the theoretical and empirical literature on the motivational power of ICT tools in differentiated teaching contexts (Benjamin, 2014; Hustinx et al., 2019; Karatza, 2019; Smith & Throne, 2007; Vargas-Parra et al., 2018), the technology-enhanced practices in this study were found to increase students’ language learning experience, i.e., learners’ behavioural, cognitive and affective engagement with various aspects of the language learning process (Dörnyei, 2019) as well as their motivated learning behaviour, i.e., the effort that they are willing to put into foreign language learning (Csizér et al., 2021). This was an important theme in the interview study, as all the participants emphasised the engaging effects of ICT tools on students–as Kamilla put it, these tools “put children on track”.

These findings reappeared in the observation study, as well, as the students in the lessons I visited appeared to be engaged and motivated when using ICT tools. I observed several examples of active, and often cheerful, participation, such as when the students were giving feedback to each other on the Flip video assignments, debating about advertisements, singing along a YouTube video, playing a drawing game on Skribbl, and doing a VR tour. The students were generally focused and on-task in these lessons and showed genuine eagerness, and, in some cases, excitement over the activities.

At this point, it is important to consider whether the increased motivation perceived by both the teachers and myself stems from the modality of ICT, the content, or a combination of both. In the interviews, modality-oriented motivation was the aspect most frequently mentioned by the teachers, as many of them highlighted the entertaining nature of ICT. However, this was only one of the perceived benefits of TEDI. As discussed in the previous sections, the participating teachers use technology in various ways and recognise multiple advantages beyond student engagement, which suggests that they view technology as a tool rather than an end in itself. For instance, Emma explained how she uses Minecraft in her Summercraft project not merely for entertainment, but as a way to practise content creation, collaborative problem-solving, and creative writing, similarly to how Kamilla uses Canva. This suggests that, while teachers acknowledge the engaging potential of ICT, they design activities where technology serves pedagogical objectives rather than mere entertainment purposes. Furthermore, the fact that the students in the observed lessons appeared engaged and on-task suggests that it was not just the digital medium but the meaningfulness of the tasks themselves that sustained their motivation. As such, these practices may be looked upon as manifestations of teachers’ technological pedagogical content knowledge (TPACK) (Mishra & Koehler, 2006), where subject-specific learning is enhanced through the purposeful integration and interplay of pedagogy and technology.

Motivated learning behaviour and positive language learning experience was not only disclosed by the teachers and observed in the lessons but were also reported on by the students in the questionnaire. These scales received remarkably high mean values (M = 4.60, SD = 0.55 for motivated learning behaviour and M = 4.42, SD = 0.66 for language learning experience), which indicates that the students taught by the participants are generally motivated and have good language learning experiences in the EFL lessons. Moreover, correlation analyses have revealed that there is a statistically significant relationship between language learning experience and some dimensions of TEDI perceptions, as correlations were found between interest-based TEDI perceptions and language learning experience (r = .57, p < .001) and between learning-profile based TEDI perceptions and language learning experience (r = .34, p = .033). This suggests that lessons in which students perceive technology-enhanced tasks as responsive to their interests and learning profiles are likely to be characterised by positive learning experiences, as well. Furthermore, regression analysis has shown that interest-based TEDI perceptions do not only correlate with but in fact have a positive influence on students’ language learning experience (β = .57, p < .001). This finding corroborates what the teachers disclosed to me and what was observed in the lessons I visited and is also consistent with prior research which showed that interest-based TEDI practices can enhance students’ language learning experience (Güvenç, 2021; Hustinx et al., 2019).

As for students’ motivated learning behaviour, the results of the correlation and regression analyses somewhat contradicted the teachers’ reports and my observations, insofar as no significant relationship was found between students’ self-reported TEDI perceptions and motivated learning behaviour. This discrepancy might be due to the complex nature of motivation, which can be influenced by various factors beyond the scope of this study. However, as is explained in Section 4.2.3.2, the lack of a significant relationship could have also been caused by the small sample size, which at times leads to insignificant coefficients and the losing of potentially important results (Dörnyei & Csizér, 2012). What can be established at this stage, nevertheless, is that the students indicated high levels of motivated learning behaviour, which suggests that while a direct link cannot be detected between this variable and the dimensions of TEDI perceptions, further exploratory studies may be able to reveal additional variables that play a role in increasing students’ motivation in technology-enhanced differentiated contexts.

Finally, students’ self-efficacy, i.e., their beliefs about their capabilities “to successfully learn a foreign language in the school context and to complete particular language-learning tasks” (Csizér et al., 2021, p. 5), was also found to improve in technology-enhanced contexts. In the interview study, both Emma and Rebeka mentioned that their students work quite confidently when using technology, and Kamilla appreciated how ICT tools can help students with autism participate in activities with more confidence.

These interview findings were supported by the results of the student questionnaire study. The scale measuring students’ self-efficacy beliefs, similar to those of language learning experience and motivated learning behaviour, received a high mean value (M = 4.52, SD = 0.51), and was found to have a significant relationship with students’ perceptions of various dimensions of TEDI. More specifically, correlation analyses revealed a positive correlation between readiness-based TEDI perceptions and self-efficacy beliefs (r = .57, p < .001) and between learning profile-based TEDI perceptions and self-efficacy beliefs (r = .43, p = .001). This suggests that lessons in which students perceive ICT use as responsive to their readiness levels and learning profiles are likely to be characterised by high self-efficacy beliefs. Regression analysis revealed that the former relationship is in fact causal, as readiness-based TEDI perceptions showed a positive impact on self-efficacy beliefs (β = .57, p < .001). This indicates that when students feel that the ICT-supported activities are in line with their readiness levels, their beliefs in their ability to successfully accomplish language learning-related tasks will also increase. This confirms the results of research which showed that certain ICT tools support readiness-based DI by offering appropriate levels of difficulty for each student (e.g., Vargas-Parra et al., 2018), and it also underlines the importance of providing students with suitable levels of challenge, which in turn may increase their self-efficacy beliefs (Bandura, 1997; Tomlinson, 2017). As Tomlinson (2001) argues, “our best teaching happens when we give students a genuine challenge and then help them successfully meet it” (p. 49). The results suggest that certain ICT tools can help in providing materials and tasks that are not only designed to be genuinely challenging but are also perceived by the students as being at just the right level for them to succeed.

The findings provide evidence that the participating teachers used technology with the explicit aim of fostering active student learning. Examples include the use of BOOKR to promote autonomous reading by allowing students to choose books in line with their interests and proficiency levels, Quizlet to offer students a choice in how they practise vocabulary, and project-based activities (e.g., Summercraft) supported by technology (Minecraft and Canva). The relatively high mean values from the student questionnaire further indicate that the observed technology-enhanced activities were suitable for fostering learner-centred use, i.e., they catered for students’ readiness levels (M = 4.27, SD = 0.62), interests (M = 4.14, SD = 0.76), and learning profiles (M = 3.97, SD = 0.77). Taken together, these findings suggest that technology was not merely present in the classroom but was actively used by the students in ways that are in line with constructivist principles, i.e., engaging them in higher-order thinking, meaningful communication, content creation, and problem-solving (Benjamin, 2014).