A Synthetic Review of MALL Research: Artifact, Environment, and Task

2.1 Artifact

In MALL research, artifacts are the tools that mediate learning and are commonly divided into two categories: digital artifacts and physical artifacts (Stockwell 2022; Kress and Selander 2012). Digital artifacts include language learning applications, multimedia resources, websites, and social media platforms that offer multimodal representations of content. These are rooted in multimodal learning theory, which posits that combining visual, auditory, textual, and interactive elements enhances comprehension and engagement (Kress and Selander 2012). The multimodal learning theories (Kress and Selander 2012) emphasize the integration of various meaning-making resources into language pedagogy. These resources may include textual, visual, auditory, and interactive elements that collectively enhance learners’ engagement and comprehension. By utilizing multimodal tools, MALL applications can create dynamic learning experiences that cater to diverse learner preferences and needs.

Physical artifacts, on the other hand, refer to the devices – smartphones, tablets, and other mobile hardware – through which digital content is accessed. While research has often focused on digital tools (e.g., Loewen et al. 2019; Parsons et al. 2022), studies such as Oberg and Daniels (2013) and Wang (2023) have highlighted how physical attributes (e.g., screen size, input methods) influence user engagement and learning efficacy. The interplay between these artifact types determines how effectively learners interact with content, emphasizing the importance of device affordances in MALL design.

Early studies often contrasted digital and traditional media. For instance, Furuya et al.’s (2004) pilot study using SMS-based grammar exercises showed learning gains; however, lacking usage data, it left the causal role of mobile mediation unclear. Other studies compared vocabulary learning on phones versus desktops, reporting longer engagement but slightly lower scores on mobile devices due to interface constraints. As tools evolved, research also became more nuanced. Loewen et al. (2019) evaluated Duolingo’s effectiveness with beginner Turkish learners. Despite measurable vocabulary gains, factors such as participants’ academic backgrounds and the mandatory nature of the course limited the generalizability of findings.

Beyond ready-made apps, researchers have advocated for learner-created and adaptive artifacts. Burston’s (2016) BYOD (Bring Your Own Device) approach had students photograph real-world items for collaborative gift-selection tasks, fostering autonomy and contextual learning. Hsu (2012) introduced multimodal vidcasting – combining images, audio, subtitles, peer feedback, and intercultural exchange – which significantly boosted engagement and learner agency. Reviews of iPad-based MALL implementations emphasize how layout, touch interaction, and multimedia convergence shape learners’ perceptions. Personalized artifacts have also shown promise: Chen and Chung’s (2008) item-response-theory system adjusted vocabulary difficulty in real time, improving learner performance and satisfaction. More recent work (e.g., Sadeghi and Khezrlou 2016) links such personalization to sustained engagement cycles and lower dropout rates.

2.2 Learning Environment

The environment dimension explores how MALL operates across formal (classroom) and informal (out-of-class or blended) settings. Learning environment considerations draw on socio-cultural perspectives (Vygotsky 1978), highlighting the influence of context on L2 acquisition. Socio-cultural theory underscores the role of interaction, scaffolding, and collaborative learning in the development of L2 skills. MALL environments provide opportunities for learners to engage in authentic communication, access learning resources in real-world contexts, and receive immediate feedback, thereby fostering a supportive learning experience.

In formal contexts, Echeverría et al. (2011) used i-mate smartphones to support collaborative physics tasks among Chilean ninth graders. The study reported high learnability and satisfaction, though observer effects may have inflated outcomes. One study involving in-service teachers found increased uptake of vocabulary apps following training, though out-of-class usage remained untracked.

Informal and blended settings often offer higher ecological validity but face engagement challenges. Nielson’s (2011) workplace study, which provided Rosetta Stone access to government volunteers, found that fewer than half logged in – and only one completed the course – highlighting the gap between accessibility and sustained use. Burden (2020) compared Quizlet-based mobile practice with paper-based word lists in a blended setting; despite grade incentives, 11 students reverted to paper, underscoring the power of habits and context.

Some studies bridge these formal/informal divides. Tay (2016), for example, implemented a 3-year iPad integration across large English classes and found that ubiquitous device access, when paired with learner autonomy, enhanced both engagement and performance. Studies by Fischer (2007) and Roussel (2011) compared self-reports with clickstream and screen-capture data, revealing notable discrepancies and advocating for privacy-sensitive analytics (e.g., LMS logs) to capture authentic usage both in class and at a distance.

2.3 Task

Tasks in MALL refer to the pedagogical activities that engage learners in using artifacts within environments to achieve linguistic outcomes. Task design principles are rooted in task-based language teaching (Ellis 2003), underscoring the importance of meaningful activities in facilitating effective second language learning experiences (Khezrlou 2024, 2021). Well-structured tasks encourage learners to use language purposefully, allowing them to practice linguistic structures in context while developing communicative competence (Abdi Tabari et al. 2024a, b). In MALL, task complexity, interactivity, and personalization play critical roles in shaping language acquisition outcomes.

Lan et al. (2007) developed a mobile-supported, peer-assisted reading system for early EFL learners. Although the sample size was small, the study showed that mobile mediation can support collaborative reading tasks. However, meta-analyses have highlighted the paucity of statistically robust task-based outcomes. This gap is echoed in Sung et al.’s (2018) finding that only one in ten cooperative MALL studies measured communicative skills.

Design-based research (DBR) has also advanced learner-centered task development. Hoven and Palalas (2013) conducted an 18-month DBR project with ESP students, iteratively refining location-based communication challenges, multimedia co-creation, and peer evaluation tasks through interviews and focus groups. Studies from 2017 to 2025 further demonstrated that wiki-mediated writing tasks – incorporating repeated drafts and synchronous/asynchronous feedback – produced gains in both explicit and implicit knowledge. Hwang et al. (2024) found that task engagement follows a cyclical pattern of active use, disengagement, and re-engagement, and that tasks offering incremental challenges and personalized feedback are key to sustaining persistence. Wang (2017) illustrated this principle through SMS-delivered grammar tasks that gradually increased in complexity, successfully restoring learner interest and yielding measurable learning gains.

In sum, these studies demonstrate how the dimensions of artifact, environment, and task have been explored, challenged, and progressively refined. Grounded in multimodal learning theory, socio-cultural theory, and task-based language teaching, this body of work offers a rich foundation for designing more effective, context-sensitive, and learner-centered MALL interventions.

3.1 Study Selection and Eligibility Criteria

The articles were selected based on their relevance to MALL, with a specific focus on design elements such as artifacts, environments, and tasks. We employed a broad set of search strings such as: mobile-assisted language learning, MALL and task design, mobile language learning and devices, digital tools and language learning, mobile learning environments. These combinations were selected to capture the major research trends across the three focal elements – artifacts, environments, and tasks – without being limited by any single term. The inclusion criteria were: (1) peer-reviewed empirical studies published between 2003 and 2024, (2) written in English, and (3) explicitly focusing on at least one of the three target elements. Meta-analyses, theoretical commentaries, and duplicate studies were excluded. An initial database search yielded 230 published articles corresponding to the specified keywords. Following the removal of 85 duplicates, 145 articles were screened based on their titles and abstracts to assess their potential relevance to the current study. During this phase, 77 articles were excluded, leaving 68 articles for further evaluation. In the third stage, a thorough full-text review was conducted, resulting in the exclusion of 49 articles that did not meet the inclusion criteria. In the final stage, one meta-analysis article was excluded (Figure 1). Ultimately, 18 articles that satisfied all inclusion criteria were selected for the review, as illustrated in Figure 1 and Table 1.

Figure 1:

Identification, screening and inclusion of studies.

To ensure inter-rater reliability, we employed investigator triangulation, wherein two independent coders with expertise in MALL and second language acquisition coded a randomly selected 30 % of the included studies. To ensure inter-coder reliability, Cohen’s Kappa coefficient was calculated, yielding a value of 0.95, indicating substantial agreement (Landis and Koch 1977). Discrepancies between coders were discussed and resolved through discussion, and the coding scheme was refined based on these discussions before being applied to the remaining studies by the first author. The coding manual is presented in Appendix A. Validity was strengthened through:

1. Triangulation of coding with published frameworks (e.g., TBLT principles, sociocultural theory),

2. Audit trails documenting all coding decisions, and

3. Use of data excerpts (e.g., direct citations, tables) to support analytical claims

4.1 Artifact

Before presenting any discussion about MALL, it is crucial to consider the artifacts encompassed by the term to set the stage for defining both the environment and the tasks that take place within it. In simple terms, it is possible to consider two forms of artifacts in MALL. On the one hand we have digital artifacts – whether a blog, a streamed online video, a social networking website, or a media sharing platform – which are composed of multiple meaning-making resources, such as visuals, music, and various combinations of modes and graphic designs, such as layout, font, linearity versus modularity (Kress and Selander 2012). On the other hand, we have physical artifacts, which includes the actual tools used in the learning process, such as smartphones, tablets, and laptops. These tools will differ in their physical and functional characteristics, including screen size, network interactivity, input methods, battery life, storage, portability, and so forth (Stockwell 2022). These physical artifacts serve as the medium through which digital artifacts are accessed and interacted with, and it is the interplay between these two forms of artifacts, digital and physical, that forms the basis of the MALL environment and influences the tasks that learners engage in.

Research into MALL has focused on both digital and physical artifacts, attempting to explore their usefulness in language learning. Of these, the vast majority of studies have looked at the digital artifacts in MALL, primarily to explore their effectiveness. There is considerably less attention given to physical artifacts in the research literature. Those that do explore physical characteristics are often of a comparable nature, looking at artifacts such as mobile phones and/or tablets to see how they compare with computers or even paper-based learning. Wang (2023), for example, found that learners had clear ideas about the affordances of engaging in activities on mobile devices compared with other means, and made informed decisions based on their experiences. Similarly, the ability of mobile devices to interact with the real world has also gained the interest of researchers, with studies such as Binhomran and Altalhab (2021) who showed that augmented reality (AR), where young learners of English used their devices to interact with a purpose-designed storybook, promoted learners’ understanding of vocabulary.

It is important to highlight that the use of digital and physical artifacts is not a standalone occurrence; rather, it relies on and is influenced by the associated teaching practice. Thus, possible divergences in pedagogical approaches can highly undermine the research results. This issue is well reflected in the study by Oberg and Daniels (2013) which demonstrated the L2 English learning gains of Japanese university students who used an i-Pod Touch-based version of a textbook program in class. Nevertheless, whereas the experimental group could study chapters at their own pace, the control group had to follow the instructor’s sequence.

The research design of some other MALL studies aiming to assess the efficacy of language learning artifacts may be significantly called into question due to their failure to take into consideration critical extraneous factors such as participants’ past achievements in learning a second language and their familiarity with theories of second language acquisition. Loewen et al. (2019) sought to investigate the effectiveness of Duolingo app in developing L2 knowledge of ab initio learners of Turkish. A mixed-methods approach, combining quantitative analysis of learning outcomes and qualitative exploration of participants’ experiences, was employed for data analysis. Results demonstrated that all participants knew more Turkish at the end of the study than when they began. However, these gains are compromised by the effect of confounding variables. The participants included eight graduate students (two master’s and six doctoral) and one professor, all of whom served as both participants and researchers, and hence their backgrounds and viewpoints impacted their learning process as well as their data analysis and interpretation. Moreover, since this research was carried out as part of a mandatory class requirement, the motivation for studying was presumably different from most Duolingo users.

Recently, there has been a tendency to encourage learners to use self-developed artifacts in MALL research. Burston (2016) advocates a Bring Your Own Device (BYOD) approach to MALL where learners can make use of their mobile devices to create personal artifacts related to assigned tasks. For instance, Burston suggests engaging students in using their mobile devices to take task-related photos:

Students could be told, for instance, that they have a certain amount of money to spend on a birthday, Christmas, or wedding gift for someone. They then do some research on the Internet and go off on their own to some shops, take pictures of possible choices, share their photos and collectively decide which gift they prefer. (Burston 2016, 7)

In a recent study, to help learners in China and Japan who are accustomed to teacher-centered school environments, Parsons et al. (2022) engaged students in vidcasting for the expression of social and cultural meaning on a more personal and emotional level through a wide array of semiotic resources. The vidcasts used resources including visual (images, photographs, and video clips), audio (recorded voices, including various prosodic elements associated with that, background music, and sound effects), and videoediting effects such as panning and zooming, transitions between images, and subtitles. Other resources employed included interactions with teachers, feedback from teachers and students, and intercultural interactions with their peers in another country. It should be noted that while self-developing and using artifacts based on a BYOD approach would involve out-of-class activities, how much of this takes place in the L2 very much depends on the competence level of the students (which requires the administration of pre-tests at the outset of a study), the ultimate requirements of the task (which should be primarily meaning-focused, yet with a necessity for the use of linguistic features that a study aims at measuring), and appropriate research methods to measure the process and product of learning. Regarding the latter, appropriate tracking of the mobile usage through language logs; case studies; post-treatment interviews; analytics tools to gather quantitative data on user progress, performance in task, areas of difficulty, and completion rates for various learning stages; and regular feedback collection from learners regarding their experiences, preferences, and challenges through surveys and questionnaires would be worthwhile. While Loewen et al. (2019) and Parsons et al. (2022) both implemented mobile apps, the latter emphasized learner-generated content and intercultural feedback, which appeared to promote higher engagement and autonomy. In contrast, Loewen et al.’s more passive use of Duolingo, despite yielding vocabulary gains, suffered from confounding variables such as participant background and external motivation.

In sum, what the results of our review regarding artifact show is that the majority of studies (e.g., Loewen et al. 2019; Parsons et al. 2022; Stockwell 2010) concentrated on digital artifacts, particularly language learning apps and multimedia tools, while studies considering physical artifacts such as device type, screen size, and portability (e.g., Wang 2023; Oberg and Daniels 2013) were sparse. Notably, physical features like device portability and input method significantly influenced learner engagement and task efficiency. Moreover, integrating learner-created digital artifacts (e.g., vidcasts in Parsons et al. 2022) demonstrated positive learner autonomy and engagement outcomes, but such implementations remain limited.

4.2 Learning Environment

Understanding the learning environment is a critical factor in the successful implementation of MALL. The design of learning environment requires careful planning and is largely dependent on the format adopted – how learners engage with content, tools, instructors, and peers (Stockwell 2022). Possible environments range from face-to-face to blended and distance learning. However, compared to the plethora of research examining the potential of mobile phones for out of class learning, has been a lesser focus on utilizing these devices as part of indoor classroom environment, despite their potential to enhance distributed practice and foster interactive learning (Meurant 2006). This is perhaps because the predominant assumption is that classroom time for formal instruction particularly in foreign language contexts is restricted, and thus mobiles can offer avenues for learners to actively participate beyond the confines of the classroom. Nevertheless, research in ace-to-face and distance environments in MALL has its own challenges in uncovering how learners utilize mobile devices for language learning purposes.

For instance, Hou et al.’s study (2024) investigating word learning in different reading environments (tablet vs. print) found that reading on a tablet resulted in less accurate word acquisition and less focused attention compared to reading printed material. Eye-tracking data showed that while higher exposure to words generally led to better learning, tablet readers engaged in less attentive reading strategies, likely due to cognitive costs associated with mobile reading. In contrast, print readers used a more focused, strategic approach, leading to better learning outcomes. These findings highlight the impact of the learning environment – whether mobile or print – on the cognitive processes involved in language acquisition and underscore the importance of understanding the influence of medium on learner engagement and learning outcomes.

It is easier to examine how learners use their mobile devices in the classroom, but at the same time, classroom usage does not necessarily reflect the natural, outside of the classroom experience, which presents a unique challenge for MALL research. Echeverría et al.’s (2011) study, for example, examined cell phone use in a face-to-face classroom setting to support collaborative work, employing i-mate SP5 Smartphone devices. Conducted with ninth-grade students at a public school in Chile, a usability analysis was adopted to examine how the hardware limitations of cell phones (e.g., processing, network, and interface limitations) impacted on collaborative physics tasks. The research measured four usability attributes – learnability, efficiency, memorability, and satisfaction. This study employed observation forms with relevant metrics to assess system performance across these usability attributes. Results indicated that phones supported collaborative work in the classroom. Memorability, for instance, was reported to be high measured by participants’ consistent response times between first and final questions. However, a note of caution about the use of observation in this study is in order here. The quick response time of the participants could have been due to their awareness of being observed in the class. Furthermore, collaboration, by its very nature, cannot be restricted to sole question answering and demands the promotion and analysis of corpora of interactions which this study failed to examine.

In spite of the fact that classroom observation has been rarely used in MALL research, it can be an effective research method, if developed intricately and employed thoroughly, to help researchers understand how mobile phones assist L2 learning. Stockwell (2019), for example, observed how students in the class discussed their mobile usage outside the classroom, and Kassem (2018) observed how following a teacher training program, in-service English teachers introduced and encouraged students to use four vocabulary learning applications. Both Stockwell (2019) and Kassem (2018) used an observation checklist to closely examine the targeted behaviors.

Although research on mobile learning outside the class might enjoy a higher level of ecological validity, it faces a number of methodological challenges. First of all, in terms of participation, students’ level of commitment may not at times be as high as in the classroom setting, mostly due to a lack of teacher’s discreet and constant presence in a platform to support learners when they face difficulties. This situation has direct implications for research, as the level of available support greatly impacts how learners interact with technology, particularly when encountering challenges (Stockwell 2022). This was documented in Sato et al.’s (2020) study which investigated the role of MALL in L2 vocabulary learning as well as learners’ autonomy in blended learning settings. The study compared Japanese learners of English who received L2 vocabulary practice through Quizlet on their mobile devices and those who used paper-based lists of expressions. To encourage the experimental group’s out-of-class learning, the teacher gave notice regarding an upcoming test for the expressions and the scores counted as part of students’ grades. Nevertheless, 11 students in the experimental group chose to learn with the paper list, possibly obtaining the list from their classmates in the control group, which the researchers failed to control. Unfortunately, however, this study fell short in obtaining any information regarding why the students preferred not to learn vocabulary through their phones. As a form of ethnographic research, case studies prove invaluable in obtaining insights about learners’ behaviors in their naturalist everyday lives.

Another issue with the use of mobile phones in distance learning environments is the difficulty of tracking mobile usage. Although in the classroom, there can be a close monitoring of the mobile usage through observation or locking mobile functions to prevent any off-task distractions, these are harder to achieve in distance learning environments. Furthermore, in some early studies, researchers did not even collect any actual usage, leaving validity of the results in doubt. In a study with Japanese university students learning English, Furuya et al. (2004) tested the impact of TOEIC practice exercises via SMS. They found improvement but did not track program usage or consider external factors. This lack of tracking undermines similar positive findings by Song (2008), who combined a website and SMS for Chinese learners’ English vocabulary. However, neither study could attribute improvements to specific factors due to a lack of usage data. Nevertheless, in recent years, to understand how mobile phones are used by teachers and learners in the broader environment, researchers have used different research methods such as longitudinal ethnographies to collect data via interviews, focus groups, surveys, observations, questionnaires, email exchanges, test scores, and so forth (e.g., Tay 2016). Although these research methods are all valuable in capturing the role of mobile devices in the development of learners’ language skills, research has shown that sometimes what learners say (and perhaps believe) they do via questionnaires and interviews do not match with what detailed tracking software actually shows. Fischer’s (2007) comparison between self-reported data and tracking data served as a convincing demonstration of this issue. Similarly, Nielson (2011) highlighted the disparity between intentions and actual follow-through in learning tasks. In a workplace-oriented study involving volunteers from various US government agencies, the opportunity to learn new languages using a popular commercial app (Rosetta Stone) was provided. Among 150 volunteers, fewer than half initiated their study by accessing their accounts, approximately 21 managed to complete the initial 50 h, and only one individual finished the final assessment of the course. Similarly, Stockwell (2010) monitored learners’ mobile usage across three four-month periods to investigate when they accessed vocabulary and listening activities via either mobile devices or PCs. The study revealed a disparity between the participants’ self-reports and their actual usage logs.

The use of data collection methods such as observation (Tanaka 2005), tracking clicks, keyboard actions, or cursor movements (Hwu 2013), and screen captures (Roussel 2011) are acknowledged to be complex approaches to tracking learners’ mobile usage and engagement due to the privacy of learners’ activities on their devices such as their web browsing, emailing, messaging, and social networking. Hence, there is a need for methods that merely track information during the performance of specific tasks that researchers are mainly interested in. Server logs such as Learning Management Systems (LMSs) that record user activity as the learners carry out activities through either their mobile devices or computers, including the type of the device, time-on-task, the scores for each task, and the answers to questions posed by the system, would put the researchers in a better position to understand each individual learner’s strengths and weaknesses in performing the tasks while safeguarding learners’ privacy during MALL task performances.

The results overall indicate that most studies prioritized informal, out-of-class learning contexts (e.g., Sato et al. 2020; Nielson 2011) with limited research on formal classroom MALL integration. Classroom-based studies (e.g., Echeverría et al. 2011; Kassem 2018) highlighted benefits like enhanced collaboration and teacher facilitation but faced challenges like observer effects and difficulty tracking in-class mobile usage. Longitudinal designs and mixed methods (e.g., Tay 2016) proved effective in capturing authentic classroom dynamics and learner trajectories.

4.3 Task

Tasks constitute the core of technology-mediated tools through which learners learn the target language skills (Khezrlou 2019, 2025; Khezrlou et al. 2017). Well-developed tasks in MALL not only facilitate language acquisition but also adapt to learners’ needs, promote engagement, and provide a pathway for practical, real-world language use. The teaching intervention using mobile devices often focuses on specific features, influencing the direction of research. For instance, mobile functionalities like real-time sharing, communication, and feedback are advantageous for communicative learning, prompting interest in enhancing language-related communication through these features (Khezrlou 2022a, 2023). More recently, research has considered the potential MALL holds for content creation and communication, where learners are required to interact with their peers and with mobile devices. Learners can record their performances, shoot videos and modify them as required by the task, communicate with their teachers through specific writing apps, etc. The work they produce can be shared with classmates, teachers, friends, and can even be published online (Morgana 2019). Many studies of MALL, for instance, have demonstrated the recent trend of investigating the use of social media in formal and informal language learning (e.g., Mompean and Fouz-González 2016). In MALL for communication, the emphasis is on the interactive process of activities like reading and writing. Learners can, for example, collaboratively read the same e-books, or write synchronously and asynchronously using sharing services such as Twitter or Instagram. Interestingly, however, Sung et al.’s (2018) meta-analysis showed that among the ten studies that they examined on mobile device utility for cooperative learning, only one (Lan et al. 2007) measured communication-related skills as dependent variables. As Burston (2015) has rightly mentioned, “over the past 20 years, statistically reliable measures of learning outcomes [in MALL research] are few and far between” (p. 16) which obviously affects the certainty of obtained results.

There is also the duration of task performance that significantly impacts the effectiveness of MALL programs. Different language skills may necessitate varied intervention lengths, demanding careful allocation of time slots to maximize mobile device functionalities. Vocabulary-related skills might benefit from brief, bite-sized tasks and short-term activities. Conversely, more complex skills like reading, listening, and writing may require more extensive time frames, considering factors such as screen size for suitability. For example, ethnographic research which is more longitudinal than other types of observational research (Polio and Friedman 2017) lend itself quiet well to the profound examination of L2 learners’ development of writing, reading or speaking skills through mobile phones. Nevertheless, there has not been much research in MALL using longitudinal research methods. For example, Tay (2016) examined learners’ use of iPads over a three-year period in order to understand the impact of mobile-assisted discussions on student engagement, lesson activities, and student outcomes. Data were gathered through perception surveys, lesson observations, and year-end examination results. Survey data consistently indicated that students perceived lessons involving iPads as more engaging. Specifically, group interviews indicated that participants’ ability to readily access the information or learning materials whenever needed influenced their active engagement and collaboration.

Tasks aimed at skill development in MALL have also benefited significantly from adaptable technologies tailored to individual learners, starting with simpler activities and gradually progressing to more challenging ones. This trend toward L2 learning in MALL mirrors the broader landscape of CALL (Lin and Lin 2019). The use of personalized adaptive systems using algorithms like item response theory (e.g., Chen and Chung 2008) or tailored to responses from activities (e.g., Cui and Bull 2005), for example through correlational studies, would provide insights into how learners may perform differently across mobile tasks of different difficulty levels. For instance, Moghari and Marandi (2017) explored texting over time as a means of gradually enhancing Iranian learners’ grammatical skills. Hence, the concept of incremental teaching in language learning serves as the cornerstone of research employing mobile devices to assist learners in building language fundamentals. Through diary studies or language logs, it is possible to examine learners’ records of their language usage or development through the performance of increasingly complex tasks using adaptive mobile-assisted tasks over time, and therefore gain valuable insight into incremental changes and personal experiences in language acquisition.

Finally, researchers need to explore deeper and broader designs to foster more innovative tasks and strengthen their effects. For instance, research concerning text messaging has primarily adopted a passive approach which is about sending messages to students for vocabulary study without investigating their reactions or learning methods. Research should explore designing functions to capture learner reactions during messaging and guide them toward active learning through suitable feedback, rather than passive reception of messages. There have been advancements or progress made in this area, though the specifics might need to be explored further. To encourage interactivity in large blended classes in China, Wang et al. (2009) evaluated the effectiveness of a mobile learning system that that was aimed at delivering live broadcasts of real-time classroom teaching to students with mobile devices. They examined an upper-level English class with approximately 1,000 students who could choose to study on campus or online anytime during a class session or the semester. Through the client program installed on students’ mobile phones, students could connect to a class, with the teacher periodically receiving a screenshot of the student’s mobile device to monitor the student’s progress. Meanwhile, students could send text messages to the teacher which were displayed on the teacher’s computer in order to inform about students’ learning progress, questions or any other feedback. This research adopted a postsurvey approach to examine students’ experience with m-learning, their perceptions of the mLearning system, their learning process and outcomes. In addition, content analysis was used to analyze text messages exchanged in this class and posts on the class’s online forum. Based on the obtained findings, the authors argued that the use of this system in the English class was successful advancing interactivity in a traditionally culturally didactic learning environment since it elicited active student participation. Wang et al. also argued that as a result of this program, students changed from passive to active participants, and voluntarily engagement in the learning process.

It is also well worth referring to Hoven and Palalas’ (2013) interesting study here since it effectively employed a longitudinal DBR to promote effective design principles for learning tasks tailored to English for special purpose (ESP) students. This study spanned 18 months and followed DBR principles, culminating in a conceptual model and design principles for an emerging mobile-enabled language learning (MELL) solution. The MELL tasks guided students through interactive processes, promoting communication with peers and native speakers within real-world language contexts. Students engaged in language challenges, co-creation of multimedia resources, and mutual evaluation of their work, including leaving comments and ratings on audio recordings. To reinforce language practice and collaboration, certain activities required students to visit Toronto sites together, supporting each other in completing communication challenges. These ESP activities aimed to enhance aural skills while encompassing all language skills to offer a comprehensive learning context. Moreover, the design of tasks adhered to the task-based teaching principles outlined by (see Ellis 2003).

The DBR used in Hoven and Palalas’ (2013) study was an interventionist participative method, applied in a naturalistic setting, to enhance educational practice through the design and refinement of innovative interventions and corresponding theory (task design principles). Through experimentation and collaboration and consultation with teachers and learners, preliminary design principles were integrated into the design of a series of tasks which were then refined based on the feedback gained from the teachers and learners through interviews, focus groups, meetings, and communication via email and the Wiggio project site. The researchers’ observations and reflections were also considered in the data analysis. In sum, the study’s meticulous design and implementation aimed to cultivate effective mobile-based language learning experiences for ESP students, aligning with task-based teaching principles. Clearly, task design research needs to rely on views of language acquisition aligned with a systematic but flexible methodology using iterative analysis, design, development, and implementation, enabling a fruitful collaboration among researchers and practitioners.

Furthermore, Hwang et al. (2024) explored how user engagement and persistence can impact the effectiveness of MALL tasks. Their study, which surveyed 3,670 adult MALL users, revealed that learners who had higher acceptance of MALL demonstrated more intense and frequent usage of language learning apps. They identified that engagement with MALL tasks often followed a cyclical pattern, involving periods of active use, disengagement, and re-engagement. Importantly, this cycle, when accompanied by higher acceptance, resulted in fewer dropouts, longer active usage, and shorter breaks between sessions. The study highlights that persistence in MALL is a multidimensional process that builds over time, reinforcing the need for engaging and adaptable tasks that support learners through various stages of engagement and re-engagement.

While numerous studies (e.g., Moghari and Marandi 2017; Wang et al. 2009) explored interactive tasks, only a few (e.g., Hoven and Palalas 2013) employed rigorous designs with longitudinal, adaptive, or progressively complex task structures. Furthermore, despite meta-analytic findings (Sung et al. 2018) underscoring the benefits of communicative tasks, most studies emphasized vocabulary learning or basic reading/listening exercises. Notably, studies with adaptive tasks tailored to learner progress (e.g., Hoven and Palalas 2013) achieved higher learner persistence and engagement (Hwang et al. 2024).