Read the following Literature and apply it to answer question 1 (a, b, c, d, e, f,g)

Educational environments fall along a continuum from physical classroom where faceto- face interactions are common to fully online learning environments where asynchronous interactions are the default. This continuum of educational environments has provided opportunities for blended and web facilitated courses where learning materials and student-instructor interactions are delivered online with little or no face-toface meetings (Allen & Seaman, 2013).

Presently, there is a recognized shift towards technology supported learning commonly known as e-learning with most of institutions of higher learning adopting elearning for fully online courses or complementary to the face-to-face sessions in blended leaming approach in order to curb the challenge of large backlog of students to be admitted (Hadullo, Oboko, & Omwenga, 2018; Luna, Castro, & Romero, 2017). As a result, there is increased number of students undertaking online learning courses (Bogarin, Cerezo, & Romero, 2018; Broadbent & Poon, 2015). The term "online learning" or as commonly known as e-learning refers to the web-based systems such as massive open online courses (MOOCs) and learning management systems (LMS) that are used by instructors to deliver learning materials and allow students to access the content and interact and obtain support during a learning episode (Delen & Liew, 2016). MOOCs are defined as open education systems for open and distance learning where students register for courses with limited admission restrictions such as prerequisite courses and selection criteria. Despite the benefits to online learning, existing literature indicates challenges that need to be addressed. First is offering adequate support and guidance to learners undertaking online learning (Kizilcec, Prez- Sanagustin, & Maldonado, 2017; Terras & Ramsay, 2015). Offering individualized support and guidance may not easily be achieved because of the large number of students enrolling on e- learning. The increased number of students taking online courses is likely to face the challenge of having enough human capacity to offer adequate support. To provide effective support and guidance to online students, we need to tap into the potential opportunities offered by educational data mining (EDM) and learner analytics (LA) tools. EDM is described as the approach of applying data mining algorithms on datasets generated from educational environments in order to understand learners and learning environments. The datasets which is in form of logs generated when learners engage to various online learning activities can be analyzed to produce inferences that can be used as indicators to provide interventions that reduce dropout rates and increase retention rates, profile learners, develop learner models, and recommender systems (Amold & Pistilli, 2012; Romero, Lpez, Luna, & Ventura, 2013; Romero & Ventura, 2007). LA on the other hand involves integration and analysis of data collected from educational environments for insights and patterns on how students engage in various learning activities during online learning. The main objective is to support students by providing interventions to

improve on undesirable learning behaviors and reinforce positive learning (Lodge, Panadero, Broadbent, & De Barba, 2019). Lodge and Corrin (2017) opine that LA provides opportunity for monitoring students' learning in order to understand their behavioral patterns and provide real- time interventions especially in online learning environments According to Naif, Ayman, & Saeed-ul (2019), the outcome from LA helps in understanding the behaviour of learners with a view of providing early intervention mechanisms that enhances learning engagement which has been found to be positively correlated to academic performance. This is likely to lead to reduced student dropout and increase the retention rates especially in higher education. In comparison, while EDM is concerned about techniques that can be used to explore data from educational environments and using the techniques to understand learners and learning environments, focusing on automated discovery of information, LA is more about analyzing and reporting insights hidden in the data about learners and learning environments, focusing on insights to "inform and empower instructors and students" (Siemens & Baker, 2012). Compared to physical classroom teaching where learners are confined together at certain periods, online learners are not restricted in managing their own schedules and learning process-what time to study and how long to engage in learning. The success of e-learning, depends on the learner's ability to take control of their own learning process (Nikolaki, Koutsoula, Lykesas, Venetsanou, & Savidou, 2017). The theory through which learners take control of the leaming process is referred to as "self-regulated learning (SRL)". Self-regulated learners are those who have the ability to take charge in managing their own learning while assuming an active role in achieving their academic goals (Zimmerman, 1990)

SRI. is grounded on different theoretical models that provide frameworks on which research studies on SRI. are carried out. According to Carlos Nez, Romera, Magno, and Panadero (2017), the popular and commonly referred models include Zimmerman's, Bocknerts, Winne and Hadwin's, Pintrich's, Efklides, and Hadwin, Jrveli, and Miller's models. Each of these models describes phases, processes, and components that can be summed up into SRI, strategies that are measured in a leaming process. The strategies include time management, metacognition, effort regulation, critical thinking, rehearsal, elaboration, organization, peer-to-peer learning, and help seeking. Learners who employ some or all of the identified strategies perform better than those with low level SRL skills and hence the need for supporting SRL. on e-learning environments, especially LMS which are majorly used by higher institutions of learning (Broadbent & Poon, 2015; Kizilcec et al., 2017; Littlejohn, Hood, Milligan, & Mustain, 2016). These strategies can be measured before, during, or after a leaming process using instruments and methods specially designed for each of the SRL models.

According to Panadero, Klug, and Jrvel (2016), measurement of SRL is believed to have undergone what is described as "three waves." These waves are identified as fust waves where self-regulation was conceptualized in terms of traits or characteristics that are inherent in learners and therefore measured using self-report tools such as interviews and questionnaires. In the second wave, SRL is viewed as events or processes that take place within a learner while being influenced by an external environment through which learning takes place. The online SRL methods, which are used to measure SRL in the second wave, allow measurement without the learner being aware. This is achieved through the use of log data collected when learners are interacting with learning environments, instructors, and fellow students. The third wave is perceived as the "current wave" where SRI measurement approaches also serve as tools to promote or reinforce the self-regulatory skills in learners.

Question one

(a) From the above literature identify three deficiencies in past literature

(b) Using the above literature develop a suitable research problem statement.

(c) Identify two research questions which you could relate to the above study.

(d) State the two hypotheses which you can use to answer your research questions.

(e) Identify:

i. Three Independent variables and

ii. One dependent variable which could be associated with this study. (4 marks)

(1) With justification explain which type of research design you could adopt to address the identified research problems in 1 (a).

(g) In your research design methodology explain the kind of data collection tool you could develop and how you will test the validity and reliability of the tool.